Forensics Talks
Forensics Talks is a series of interviews with Forensic Professionals from different disciplines around the globe. Learn about science, technology and important cases where Forensic Science has played an important role.
Forensics Talks
EP 111 | Henry Vega | Forensic Audio Analysis
In this episode of Forensics Talks, we're tuning in to the science of sound with Henry Vega, Forensic Engineer at JS Forensics Consulting.
Henry will walk us through how dashcam audio recordings can be used to calculate vehicle speed, how tire frequencies are analyzed in real-world crash cases, and how firearm discharges captured on officer bodycams are helping uncover key evidence. We'll also explore his collaborations with experts around the world pushing the boundaries of audio-based forensic reconstruction.
If you’ve ever wondered how much information is hidden in a sound file — this is the episode for you.
Originally aired on: August 7, 2025
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Hey, everyone, it's Eugene Liscio here.
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And welcome to Forensics talks.
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Today is all about audio analysis.
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And this is Forensics Talks episode 111.
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So my guest today is Henry Vega.
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And we're going to get into that
in just a second.
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Now just so everybody knows
this is a prerecorded video.
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And so sometimes we do this.
We don't do too many.
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We normally do them live.
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But nonetheless while we air this you can
still put comments in the chat window.
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And, we can comment back,
but the video itself is recorded.
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So if you are online, go ahead and do what
we usually do and type
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in where you're from.
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Type in the city or the country
where you're watching from.
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It's always great to see where people are,
while they're watching this.
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The second thing is
you can always comment, right?
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So if you have any questions for our guest
today, please go ahead and do that.
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And then finally, you know, if you like
and subscribe and do that thing,
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that'd be fantastic.
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And you can always find out when there's
a new episode of Forensics Talks.
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So let's get started
on this particular episode,
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and I'd like to do a little brief
introduction here.
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So today's guest is Henry Vega.
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Henry is a forensic engineer
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with JS Forensics Consulting,
and he's based in Illinois.
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He's a licensed professional mechanical
engineer and actor,
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accredited accident reconstructionist
and FAA certified UAV pilot,
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and an associate member of the Audio
Engineering Society.
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So we're going to be talking about
audio today.
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He holds a degree
in mechanical engineering
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from Northern Illinois University,
and he's worked in failure analysis
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and accident reconstruction since 1998.
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Now, Henry brings a broad
range of expertise in analyzing crashes
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involving commercial vehicles,
motorcycles, pedestrians and more.
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His research and publications in forensic
audio have explored calculating
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vehicle speeds using dash cam audio,
and he's currently collaborating
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with experts worldwide on projects in involving tire acoustics,
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firearm audio from body cams and roadway
analysis across the UK.
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He combines drone imagery, 3D
laser scans and advanced modeling tools
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like PC crash and 3ds Max to reconstruct
complex incidents with precision,
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and I've been fortunate
to work with him on a previous project.
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We were looking at audio
from body cameras for firearms analysis.
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So let me bring him in here
and then you guys.
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Hey, Henry,
how are you doing and doing well.
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Okay, well, great to have you here.
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And, we have, we've got a little bit
of a working relationship here.
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So just for full disclosure, people know
that we kind of know each other
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fairly well,
and we've done some work together, so.
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Yeah, I'm happy. Happy
I got you on here finally.
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We've been talking about it for some time.
That is true.
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So first thing, I want to talk
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or give people a sense of your background
and kind of who Henry is.
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So I often start early
on, you know, if you go back to,
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you know,
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what, you were in school and, you know,
thinking about what you were doing,
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were you always headed
towards the engineering, feel like,
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is this
where you thought you would be originally,
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or were you on a different path?
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And it was certainly in a different path.
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And it turns out that I was interested
in architecture, and eventually
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I found myself
drawn towards the math and science aspect,
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and I honestly thought I was going to be
doing some kind of design.
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And as you can see, I'm not doing design.
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I'm actually doing forensic analysis.
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Yeah, yeah.
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Did did, so did somebody introduce you
to somebody, motivate
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you into the engineering field
or how did that come about actually,
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that is interesting.
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When I was interested in architecture,
I realized that
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there were some kids in my class,
that were working on
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electrolysis, that were separating oxygen
and hydrogen from water.
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And just the simple concept of doing
that is interesting itself.
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But these individuals
were like in fifth grade,
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and I thought, there's got to be more to
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this whole,
aspect that I need to look into.
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And ever since then,
I took an interest in science,
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in math, and eventually found myself
moving away from this concept
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of designing homes
to maybe working on science projects.
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And that's what I pursued in college.
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Okay.
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So yeah, so you ended up being,
well, let's see, it sounds like you were
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sort of a technical kid.
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I mean, yeah, I of course, architecture
is creative as well.
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There's a creative side to, I think
architecture, but it's technical too.
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So you know, it's interesting
you have sort of both of those interests.
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Now when you took
the mechanical engineering,
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did you jump into forensics right away
or was there a different,
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area you're working in?
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No, no, I, I had no idea,
what mechanical engineering had entailed.
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I just knew that it was very broad,
and it offered, a wide range of,
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possibilities from automotive to.
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Structural aspects, I guess.
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But when I was in college,
I realized, well, at university,
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I guess it when I was in the university,
I realized that.
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I didn't have any friends or family
in the engineering field.
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So as a result of that,
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I decided to go off on my own
and and learn how to do 3D, 3D, CAD,
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because 3D CAD at the time was new
and people were switching from
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from the forward
two to working in actually CAD systems.
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So I ended up learning 3D, CAD,
and I went to various engineering firms
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when I was going through college
and offered my services.
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As a result, I ended up working in
the mechanical engineering area for civil
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electrical.
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And I realized
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I could basically do
any kind of engineering I wanted.
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And then I thought,
do I really want to just do mechanical
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or is there something I want?
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I would like to focus on?
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And it wasn't until I graduated
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college, about a month before college
that a, forensic company
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came to our university
and talked about how forensics was taking
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Z to A.
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So in other words, engineering
typically goes for me to Z.
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You start with a process
and then you work your way.
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But in forensics you're you're starting
from Z and working backwards.
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And the whole concept
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was just so fascinating that I thought,
this is what I want to do.
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I couldn't believe
there was even a job for this.
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I couldn't believe
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people are actually getting paid to to
to put back together these pieces.
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And keep in mind
that back then in the 90s,
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the whole concept of forensics
just didn't exist.
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Those shows weren't
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very popular at that time,
so I didn't know what forensics meant.
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But once I realized I, I was like, I need.
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Yeah, part of it.
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Yeah. Yeah,
I kind of had the same experience.
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I remember, and same thing as you like,
I got into 3D modeling and some of that.
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Actually.
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I'm just curious,
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what were some of the early programs
because I started on AutoCAD in university
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and then there was like SolidWorks
and Unigraphics and
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and there was all these other programs.
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You remember what you were using?
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Yes. I ended up,
I was exposed to CAD, AutoCAD back
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in, 1988
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And then I was exposed to CADkey,
which was more three dimensional,
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and I, I sucked it up so well
that I ended up learning
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how to do, Lisp, which is the programing
language of AutoCAD.
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And I thought maybe I want to do
a little bit more than just be
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the drafter or to work as a designer.
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I want to actually know
how this all actually comes together.
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Yeah.
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For sure.
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And you you started with
when did you start with
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JS Forensics in JS
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2021, I believe.
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Yes, 2021.
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I ended up starting with, JS Forensics.
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And, before that,
you had already gotten, like, your,
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for those that don't know, ACTAR
is a kind of certification for,
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like, accident reconstructionists
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and, you were already doing
some other stuff in the field, right? Yes.
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Prior to coming, with JS Forensics,
I had already obtained my license
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as well as the ACTAR and I'd already done,
crash testing and product testing.
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So we were doing
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we were burning vehicles,
but for the research purposes, of course.
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Doing
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component testing, which I ended up doing
when I was down in Texas.
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And I had the opportunity of working
with a, massive pendulum where you
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attach a piece of
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componentry, whether it's a vehicle
and or not,
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and then you just subjected
with a battering ram.
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It was, I believe, about two stories high.
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And when you're working on
something like that, you need
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you needed a team of individuals
to make it work smoothly.
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So it was
it was interesting and it was fun.
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Yeah. For sure.
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I guess I should give a shout out
to, Jeff Suway, who is, I guess the,
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you know, the J and the S
in the JS Forensics. Yes.
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And he was a past guest
also on forensics talks.
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And he's also been a speaker
at the Forensic Photography
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Symposium in the past.
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So he was talking a lot more, focusing
on lighting and that sort of thing. So.
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So, Jeff, if you're listening,
thanks a lot. Appreciate it.
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Yeah.
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Yeah.
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So let's let's start
getting into the audio here.
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So what was your first sort of exposure
to the whole audio.
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And when, you know, you were like,
hey, this, this is a thing here.
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Like this is something that I can do.
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Tell me about the introduction to audio.
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Interesting.
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When years ago,
I ended up, working on a case.
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It was an officer
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involved shooting, and my my scope was
was nothing related to audio.
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It was to simply take an image
of a body camera
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and decomp a bit,
which basically means, removing the blur.
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And then I ended up, writing a program
in Matlab
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where I was able to remove the blur.
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And I noticed something that I thought was
interesting.
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It looked like
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there was more to the story.
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And when I approached the,
the attorney, hired me, I was surprised
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to find out that there were
there was far more data available.
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For example, there were body cameras
Four different body cameras
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with video and audio.
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So as a result of that, I thought,
why not look at the,
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look at the video,
but let's see what the sound has to say.
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And I bring this up
because for the most part, individuals,
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when they when they're giving video,
I'll be the first to admit it.
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I want to see what it has.
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What what has depicted...
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The visual component, you mean. Right?
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The visual component, but the audio is,
is almost, left to the side.
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And it all started with the simple fact
I asked how many bullets were discharged
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and they thought,
well, maybe five or maybe six.
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And I thought,
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there has to be a definitive number,
and there's one way to actually do that.
00:11:01:13 - 00:11:04:13
And prior to that, I've had an interest
in vibrational analysis.
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So I thought, take the audio
and look at it in the spectrogram
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where you can actually, filter out,
the different types of frequencies.
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And I was truly impressed.
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I had never seen,
ballistics, audio before,
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and you could definitively
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see how many discharges, but
not only how many discharges, the timing.
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So now you had timing
and there was another component
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I found even more fascinating,
which was location,
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because the discharge of a firearm
in hallway versus the discharge in an open
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room will have a different signature,
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especially if they both have
the same firearm, the same ammunition.
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And that's when I realized
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I could do far more if I have the audio.
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And if you take into account
the photographs that were taken
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at the scene, along with the laser scans,
I ended up doing trajectory.
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So now we have to trajectory
and we have sequence.
00:11:59:25 - 00:12:04:14
And as a result of that,
I decided to approach the attorney and
00:12:04:26 - 00:12:08:09
and asked to do more
than just the deconvolution.
00:12:08:09 - 00:12:08:24
In that case.
00:12:10:00 - 00:12:10:20
Interesting.
00:12:10:20 - 00:12:11:25
Yeah, that's pretty cool.
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And so that's the start.
00:12:13:02 - 00:12:15:21
And then from there,
it sort of took you on to,
00:12:15:21 - 00:12:19:00
like you've got a couple of papers
and we'll bring them up in a second here.
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But when,
when did it go over to the automotive side
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or like the accident reconstruction side,
how did that how did that slip over?
00:12:26:17 - 00:12:30:06
You know, when I tell people this story
and they told me you really should work on
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and and a better story, but it's the story
that you can't change it.
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I was on my way to work one day,
and I realized that every time I went over
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a road, roadway bridge with concrete
expansion joints, I heard a thump, thump.
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And when I heard that, I thought,
what are the chances
00:12:47:20 - 00:12:50:16
I can actually calculate
the speed of my vehicle
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if I knew the timing
between those two setups.
00:12:54:02 - 00:12:57:09
So at the time,
I didn't have a dash camera,
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and that's
when I purchased the dash camera.
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It was a low quality dash camera.
00:13:01:28 - 00:13:05:07
Nevertheless, the audio was superb
00:13:05:23 - 00:13:10:12
because by bringing the audio in the
spectrogram, I was able to see the actual,
00:13:12:15 - 00:13:15:12
frequency, impulses
00:13:15:12 - 00:13:18:12
which were depicted in vertical
and in a vertical manner.
00:13:18:19 - 00:13:23:11
The first one denoted the front axle,
the second impulse, the rear axle,
00:13:23:23 - 00:13:27:20
and the amount of data that you could
capture was really fascinating
00:13:27:20 - 00:13:32:09
because even though you had wind,
the wind was at a different frequency.
00:13:32:09 - 00:13:35:17
So when people criticized
that this couldn't be done,
00:13:36:02 - 00:13:39:13
I didn't think I was under the impression
they didn't have the concept
00:13:39:13 - 00:13:42:13
that it could be valued in the spectogram,
because for most people,
00:13:42:20 - 00:13:47:13
when they think of audio, they think of,
a time domain, which as time progresses
00:13:47:13 - 00:13:50:14
you have valleys and peaks,
but in a frequency domain
00:13:51:06 - 00:13:54:06
it's a different,
just a different avenue.
00:13:54:08 - 00:13:57:28
And at that point, I approached, Jeff
and I told him
00:13:57:28 - 00:14:02:11
I would like to do research in an audio,
and he said, run with it.
00:14:02:24 - 00:14:03:24
What do you need?
00:14:03:24 - 00:14:06:03
And he provided me everything I needed.
00:14:06:03 - 00:14:09:25
I ended up purchasing a much
better equipment than I currently had,
00:14:10:07 - 00:14:13:08
and then I ran with it,
and I realized you could.
00:14:14:00 - 00:14:17:17
And from that point,
I didn't only realize that you could do
00:14:17:29 - 00:14:21:15
speed from a wheelbase of a vehicle
if you had the timing,
00:14:21:25 - 00:14:26:23
but you could also calculate the speed
of a vehicle going over rumble strips.
00:14:27:06 - 00:14:29:24
If you take into account
the beats phenomena,
00:14:29:24 - 00:14:34:17
which I had known from this interest
I had with vibrational analysis.
00:14:36:02 - 00:14:39:02
That paper,
I thought was just going to end there,
00:14:39:05 - 00:14:42:08
and it didn't, because during the research
00:14:42:24 - 00:14:46:13
there was a frequency
that seemed to change with vehicle speed.
00:14:47:03 - 00:14:50:19
And I spent the next, couple of years
trying to determine
00:14:50:19 - 00:14:53:05
how that equation,
how I could actually come up
00:14:53:05 - 00:14:56:05
with a mathematical equation
to model that.
00:14:56:18 - 00:14:58:15
And eventually
00:14:58:15 - 00:15:00:28
the second paper
which you had mentioned came up.
00:15:00:28 - 00:15:03:28
And I'll go into more detail,
but if you have any questions,
00:15:03:28 - 00:15:07:16
and what I've done in the first paper,
please, please ask me.
00:15:07:24 - 00:15:09:19
Well,
I'm going to ask you in a second here, but
00:15:09:19 - 00:15:11:03
actually you mentioned something about,
00:15:11:03 - 00:15:12:28
you know,
you said like you upgraded your equipment.
00:15:12:28 - 00:15:14:21
Do you have any thoughts on
00:15:14:21 - 00:15:18:17
or could you maybe just give some of us
a little bit of insight into the,
00:15:18:17 - 00:15:20:24
the hardware
and the software that you use?
00:15:20:24 - 00:15:23:05
And this is from a testing perspective,
because
00:15:23:05 - 00:15:26:05
when you're on the receiving end, it's
it could be a body camera or whatever, but
00:15:26:26 - 00:15:28:07
have you found that
00:15:28:07 - 00:15:31:01
there's a camera or something
that has really good audio
00:15:31:01 - 00:15:32:07
and like you're surprised by it?
00:15:32:07 - 00:15:36:09
Or and what about the software
that you're using to okay, so when I,
00:15:36:09 - 00:15:39:09
when I took the audio
from the dash camera,
00:15:39:15 - 00:15:44:20
I ended up getting a, a low quality,
I shouldn't say low quality.
00:15:44:29 - 00:15:48:02
It was it was a lower end, dash camera
00:15:48:25 - 00:15:53:05
and the dash camera had sufficient audio.
00:15:53:05 - 00:15:55:13
It was recording a 32, hertz
00:15:57:01 - 00:15:57:28
on the audio
00:15:57:28 - 00:16:01:21
channel,
and it was enough to pick up minute detail
00:16:01:23 - 00:16:05:11
so that you can possibly obtain,
using, photogrammetry methods.
00:16:07:09 - 00:16:10:09
I then ended up obtaining, GoPros to,
00:16:10:12 - 00:16:13:20
to compare up against, the,
the dash camera.
00:16:14:20 - 00:16:18:22
And then I ended up getting a high quality
Rhode wireless microphone.
00:16:19:17 - 00:16:23:14
I realized that regardless
of what dash camera
00:16:23:14 - 00:16:29:14
I was using or what recording medium
audio had a tremendous amount of data,
00:16:29:25 - 00:16:33:20
whether it was a low quality dash camera
or whether it was a high,
00:16:33:20 - 00:16:36:20
a higher quality, a GoPro.
00:16:36:24 - 00:16:39:03
I took the audio
00:16:39:03 - 00:16:41:14
and I separated it from the video,
00:16:41:14 - 00:16:46:18
and then once I inserted it into, Izotope,
which Izotope allowed me
00:16:46:18 - 00:16:50:15
to visualize information
and not alter it in a way that,
00:16:51:27 - 00:16:54:27
when we
consider spoliation as a result of that,
00:16:54:27 - 00:16:58:14
I was able to develop a method
which I then, shared with,
00:16:58:18 - 00:17:02:22
the two individuals down in, California
and, Justin Ngo
00:17:02:25 - 00:17:05:17
and Ziad Hatab
00:17:05:17 - 00:17:08:27
I showed them and they helped me
bring it in
00:17:08:27 - 00:17:12:21
together and we,
we made a really good, paper on that.
00:17:12:28 - 00:17:15:02
So what kind of what kind of frequencies
are we talking look at?
00:17:15:02 - 00:17:16:13
What's the range of frequencies
00:17:16:13 - 00:17:19:19
that you're typically picking up
with these kinds of devices.
00:17:19:19 - 00:17:21:19
And is it just because there's enough
00:17:21:19 - 00:17:25:08
bandwidth in there that we can get the low
and the high frequencies and everything
00:17:25:08 - 00:17:28:22
in between, that you're able to separate,
you know, different frequencies out?
00:17:29:09 - 00:17:33:11
Yes. Well, when you're dealing with a,
spectrogram, what you're essentially doing
00:17:33:11 - 00:17:35:29
is you're taking the time domain,
which I had mentioned before.
00:17:35:29 - 00:17:38:22
You have as time progressed,
as you have valleys and peaks,
00:17:38:22 - 00:17:42:05
when you do, a fast Fourier transform,
which is, a way
00:17:42:05 - 00:17:46:14
of taking all of the frequencies
and separating them out, like in bins.
00:17:46:25 - 00:17:50:23
And then you, you, you stack them together
and you have a timeline of,
00:17:51:13 - 00:17:53:08
frequencies and time.
00:17:53:08 - 00:17:56:08
So time is on the horizontal line and,
00:17:56:08 - 00:17:59:24
vertically, on the vertical horizon,
you have the different frequencies.
00:18:00:05 - 00:18:04:26
And the intensity
coming out of that 2D image is the,
00:18:06:08 - 00:18:09:06
the, the intensity of the amplification,
00:18:09:06 - 00:18:12:06
or the strength of the frequencies
itself.
00:18:13:00 - 00:18:14:18
The frequencies I was dealing with
00:18:14:18 - 00:18:17:18
was were between zero and,
00:18:17:20 - 00:18:18:17
2000.
00:18:18:17 - 00:18:23:09
Within that, within that range,
I was able to see the difference
00:18:23:09 - 00:18:27:01
between
the wind that the vehicle was recording,
00:18:27:12 - 00:18:30:28
which was at the lower end frequencies,
and then you had the higher frequency,
00:18:30:28 - 00:18:33:28
which is the frequency
that was being created by the wheels.
00:18:34:19 - 00:18:37:00
As they were turning on the roadway.
00:18:37:00 - 00:18:41:27
But when you're going over roadway,
you know, I'm only you'll
00:18:41:27 - 00:18:44:27
you'll notice that you get impulses,
which is a vertical,
00:18:45:18 - 00:18:49:11
cascade of, of, frequencies
from the beginning all the way to the top.
00:18:49:11 - 00:18:52:12
So it's like a, it's like a vertical line,
you know, exactly where it happens
00:18:52:21 - 00:18:55:29
when the front wheel interacts
with a concrete expansion joint
00:18:56:02 - 00:19:01:20
and the rear expansion joint, the rear
wheels, contact the, expansion joint. So
00:19:03:03 - 00:19:04:24
anything between 0 and
00:19:04:24 - 00:19:07:24
2000 frequencies, to that 2000Hz was,
00:19:08:27 - 00:19:13:25
was well within what any kind of,
dash cameras could possibly, capture.
00:19:13:25 - 00:19:15:07
And it did an amazing job.
00:19:15:07 - 00:19:18:22
So you don't need high end equipment
to record it.
00:19:18:22 - 00:19:19:10
Got to the point
00:19:19:10 - 00:19:23:24
where I was even using my, my mobile phone
to to take, sound measurements.
00:19:23:24 - 00:19:26:28
And I realized there is
00:19:27:01 - 00:19:30:10
you really don't need high sophisticated
equipment to, to make recordings.
00:19:30:10 - 00:19:32:14
You just simply need a simple dash camera.
00:19:32:14 - 00:19:35:21
And if you happen to be leaving
a voicemail as you're going over
00:19:35:21 - 00:19:39:12
the roadway, that also works
because I checked it out and it works.
00:19:40:03 - 00:19:40:27
Oh that's interesting.
00:19:40:27 - 00:19:43:13
I didn't think about leaving a voicemail.
I mean, you're still recording.
00:19:43:13 - 00:19:45:24
That's a very good point there. Nice.
00:19:45:24 - 00:19:48:24
Let me bring up these papers
so that people can see,
00:19:49:00 - 00:19:52:02
there's a couple papers
that we're going to be talking about.
00:19:52:02 - 00:19:55:10
So one is from, April 2023.
00:19:55:10 - 00:19:57:13
The other one is from April 2025.
00:19:57:13 - 00:20:00:03
So almost like two years apart
for getting these things published.
00:20:00:03 - 00:20:03:03
But could you, could you go through the,
00:20:03:03 - 00:20:08:27
basically what you did on the first one
and then how that led into this second one
00:20:08:29 - 00:20:11:29
and kind of like overall,
give a summary of your findings.
00:20:12:22 - 00:20:15:24
So as I mentioned before,
I was driving down the roadway and I
00:20:15:24 - 00:20:18:12
and I wondered whether or not
actually calculate speed
00:20:18:12 - 00:20:21:13
using the wheelbase of a vehicle
if I had a recording device.
00:20:21:24 - 00:20:24:24
That's what brought about the first paper,
which was, to,
00:20:25:01 - 00:20:30:22
2023-01-0632, that speed determination
00:20:30:26 - 00:20:35:06
using audio analysis of dash camera video
for vehicle action reconstruction
00:20:36:06 - 00:20:36:22
using.
00:20:36:22 - 00:20:39:22
Back then I ended up using a,
00:20:40:17 - 00:20:44:05
a, Nextbase
00:20:45:07 - 00:20:48:17
camera and as well as a GoPro cameras
00:20:48:17 - 00:20:52:01
to, try to determine, audio.
00:20:52:17 - 00:20:56:19
And I ended up using, Vbox
to capture the ground truth speed.
00:20:56:20 - 00:20:59:20
So when you have the ground truth speed
00:20:59:24 - 00:21:02:25
and you have the audio, you have something
to compare it up against.
00:21:03:08 - 00:21:09:02
By doing so, I was able to determine that
there, there was somewhere in the order
00:21:09:02 - 00:21:12:15
of, I believe, a three mile,
plus or -three
00:21:12:15 - 00:21:15:15
mile per hour, accuracy there.
00:21:15:24 - 00:21:21:10
And this roadway was ideal
because you had nine expansion joints.
00:21:21:10 - 00:21:24:21
So I was able to increase my speed
and decrease my speed
00:21:25:07 - 00:21:29:00
and test various locations
using that same roadway.
00:21:29:00 - 00:21:29:23
And now that happened.
00:21:29:23 - 00:21:33:12
That roadway happens to be the goal
two place for me to do, testing.
00:21:35:04 - 00:21:35:17
Would you
00:21:35:17 - 00:21:38:17
mind scrolling down a little bit more?
00:21:38:25 - 00:21:43:08
I, I then eventually realized
that if it worked for,
00:21:43:27 - 00:21:48:18
a Nextbase, I was wondering if it worked
for other types of dash cameras.
00:21:48:18 - 00:21:53:15
So I reached out to, Justin,
Ngo, out in California
00:21:53:15 - 00:21:56:15
and asked him to conduct,
testing over rumble strips.
00:21:57:25 - 00:21:59:26
He found a rumble strip area,
00:21:59:26 - 00:22:02:07
and he drove over it
00:22:02:07 - 00:22:05:11
at a known speed, being recorded by VBox.
00:22:05:29 - 00:22:09:20
And then we applied,
this whole concept of the beat phenomenon
00:22:10:01 - 00:22:13:03
to try to establish how we could calculate
vehicle speed using,
00:22:14:05 - 00:22:15:11
the rumble strips.
00:22:15:11 - 00:22:17:16
And it worked just as well.
00:22:17:16 - 00:22:21:07
So it's a very accurate method
00:22:22:02 - 00:22:25:16
that doesn't require
sophisticated equipment other than
00:22:26:18 - 00:22:28:04
a recording medium.
00:22:28:04 - 00:22:30:24
Now, in the paper that you that you have
00:22:30:24 - 00:22:34:21
in front of me, which is the paper
I'm just describing, has examples of what
00:22:34:21 - 00:22:39:04
the impulses look like,
you have vertical, frequencies
00:22:39:24 - 00:22:43:22
extending from zero up
to about 500, hertz.
00:22:44:13 - 00:22:47:09
And you can see when they begin
and when they
00:22:47:09 - 00:22:50:21
when the first impact happens
and then the second impact happens.
00:22:50:29 - 00:22:54:14
We also ended up using horn,
00:22:54:25 - 00:22:57:18
to, to establish synchronization
00:22:57:18 - 00:23:01:07
of the cameras and microphones
as well as a clapperboard.
00:23:01:21 - 00:23:05:18
So what I mean by
that is in order to synchronize the audio
00:23:05:18 - 00:23:09:05
between the different types of cameras
that we were using,
00:23:09:23 - 00:23:13:19
I decided that we should use the
the clapperboard,
00:23:13:22 - 00:23:17:17
which everyone has probably seen
in in movies where they say lights,
00:23:17:17 - 00:23:21:09
camera, action and they slap this board
that itself created
00:23:21:09 - 00:23:26:08
a, an audio cue which allowed us
to synchronize the, the data.
00:23:27:06 - 00:23:29:17
But then I also ended up using the horn.
00:23:29:17 - 00:23:32:29
And then that's when you start realizing
that the horn has its own signature
00:23:33:09 - 00:23:35:22
and that the clapperboard
has its own signature.
00:23:35:22 - 00:23:38:22
And if that's not even interesting,
I want to let you know that
00:23:38:24 - 00:23:42:18
the turn signals have their own signature,
and then you have acceleration.
00:23:43:07 - 00:23:45:21
It's what I initially thought was supposed
00:23:45:21 - 00:23:49:01
to be very simple,
has now turned into a project
00:23:49:01 - 00:23:52:29
that has taken a life of its own,
because there's so many avenues.
00:23:53:18 - 00:23:57:06
I have been contacted by attorneys
to determine whether or not
00:23:57:06 - 00:24:01:26
their client had their turn
signal turned on prior to a collision, and
00:24:03:06 - 00:24:06:15
if you look at it in the spectrogram,
you have a definitive
00:24:07:14 - 00:24:10:22
signature and you also know
when the impact takes place.
00:24:11:00 - 00:24:14:00
So now you know how how long prior
00:24:14:00 - 00:24:17:10
to the collision,
the turn signal was actually turned on.
00:24:17:15 - 00:24:21:17
And that is probably impressive
in my opinion, because in the past
00:24:21:17 - 00:24:27:06
you just had to go off of eyewitness
testimony or the testimony of the driver.
00:24:27:06 - 00:24:29:01
But now you have an actual
00:24:30:21 - 00:24:31:07
diagram
00:24:31:07 - 00:24:34:07
that you can actually say, yes,
that is when it happened.
00:24:34:09 - 00:24:34:21
Right?
00:24:34:21 - 00:24:38:04
It's a real objective, more objective
method of of doing that sort of thing.
00:24:38:13 - 00:24:41:20
So what when you when you finally did this
and you're like, hey, we can
00:24:41:27 - 00:24:46:11
we can get speed, within a reasonable
margin of error or whatever.
00:24:46:17 - 00:24:48:21
So what questions opened up for you next?
00:24:48:21 - 00:24:50:18
And what led you into the next paper?
00:24:50:18 - 00:24:52:03
Let me flip on to that one here.
00:24:52:03 - 00:24:55:21
So speed determination
using audio analysis of dash camera video
00:24:55:21 - 00:24:59:28
from passenger vehicle tires frequencies
for vehicle accident reconstruction.
00:25:01:07 - 00:25:05:02
So after the first paper was written,
I noticed that
00:25:05:09 - 00:25:08:10
there was a frequency that just kept
changing with vehicle speed.
00:25:08:14 - 00:25:13:29
Except it wasn't a simple calculation
of taking the rotational,
00:25:14:18 - 00:25:18:04
velocity of the passenger vehicle
that we were testing.
00:25:18:04 - 00:25:20:24
It just wasn't. It's
nothing, was it? It wasn't lining up.
00:25:20:24 - 00:25:23:09
The frequency was much too high.
00:25:23:09 - 00:25:25:14
And I reached out to,
00:25:26:25 - 00:25:28:13
Tony Cornetto of Momenta, and
00:25:28:13 - 00:25:31:21
I told him about this, and he's the one
who came to this conclusion.
00:25:31:21 - 00:25:32:29
You know what? I bet it has.
00:25:32:29 - 00:25:37:14
I bet it's a a, speed,
a speed trace of a vehicle.
00:25:37:21 - 00:25:40:14
But I explained,
but it's not making sense.
00:25:40:14 - 00:25:43:14
So if it has something to do with it,
maybe
00:25:43:24 - 00:25:45:11
maybe it has something
to do with the tires.
00:25:45:11 - 00:25:48:11
Maybe it has something to do with,
with with the gearing system.
00:25:48:25 - 00:25:52:13
So we started throwing all these ideas out
and I came back
00:25:53:02 - 00:25:55:03
and I focused on the tires.
00:25:55:03 - 00:25:57:24
And it turns out that the tires actually
00:25:57:24 - 00:26:00:24
play a major role,
because if you think about it,
00:26:01:17 - 00:26:04:17
a tire is like a gear
rolling down the roadway
00:26:05:08 - 00:26:07:23
every time that gear makes contact.
00:26:07:23 - 00:26:09:05
Those are the teeth.
00:26:10:03 - 00:26:11:26
And the
gear make contact with the roadway.
00:26:11:26 - 00:26:14:06
It makes a sound.
00:26:14:06 - 00:26:16:04
So now that made sense.
00:26:16:04 - 00:26:19:20
But yet really, the frequency
still wasn't matching up.
00:26:20:06 - 00:26:23:06
So I thought it has something
to do with the tire blocks,
00:26:23:24 - 00:26:26:06
but something's still not making sense.
00:26:26:06 - 00:26:29:12
And then when you look at the tire
in in a more detailed manner,
00:26:29:12 - 00:26:32:12
you realize that the tire blocks
on the inside of the vehicle
00:26:32:24 - 00:26:35:27
versus
the outward are not perfectly in line.
00:26:35:27 - 00:26:37:09
They're offset.
00:26:37:09 - 00:26:40:08
So now if you have, let's say,
00:26:40:08 - 00:26:43:06
50 tire blocks on the outer,
00:26:43:06 - 00:26:45:06
you have to count the 50 on the inner.
00:26:45:06 - 00:26:49:01
So now you're looking at a 100 tire block,
not just 50.
00:26:49:15 - 00:26:52:15
And then once I took that to account,
00:26:52:23 - 00:26:55:23
the equation
matched up with the frequency trace.
00:26:56:00 - 00:27:00:25
But it still was off by,
about a five mile per hour difference.
00:27:01:06 - 00:27:04:22
So I reached out to Lou Peck,
and I showed in my research
00:27:04:22 - 00:27:08:28
because I needed to, to consider
doing research with us on motorcycles
00:27:08:28 - 00:27:11:28
because he happens
to be the motorcycle guy.
00:27:11:28 - 00:27:16:06
And then that's when he recommended,
what are the chances
00:27:16:21 - 00:27:21:06
that it's not the radius, that it's
actually the circumference of the tire?
00:27:21:19 - 00:27:25:06
Because if you take into account
the radius and the number of tire blocks
00:27:25:06 - 00:27:29:04
and the frequency, put it into equation,
which I,
00:27:29:24 - 00:27:32:24
I wanted to start,
referring to it as the Vega equation.
00:27:33:14 - 00:27:36:07
You can calculate a speed, but
00:27:36:07 - 00:27:39:08
when Lou looked at it, he said,
what are the chances?
00:27:39:08 - 00:27:41:18
It has nothing to do with radius
what it has?
00:27:41:18 - 00:27:43:11
What if it has something
to do with circumference?
00:27:43:11 - 00:27:48:24
And if you think about it, a load
a tire is no longer round, right?
00:27:48:24 - 00:27:50:01
It's actually deform slightly.
00:27:50:01 - 00:27:53:01
So I went out and got a, a seamstress.
00:27:53:11 - 00:27:55:13
Tape measure wrapped around the tire.
00:27:56:19 - 00:27:57:03
And lo and behold
00:27:57:03 - 00:28:00:16
when you take into account
that minor difference, it actually
00:28:01:03 - 00:28:04:03
made the equation that much more accurate.
00:28:04:03 - 00:28:06:27
So now this equation, if you know
00:28:06:27 - 00:28:09:27
the circumference of the tire,
the number of tire blocks
00:28:11:00 - 00:28:13:23
and the frequency
pulled from the spectrogram,
00:28:13:23 - 00:28:17:06
you have speed
and you don't have just five seconds.
00:28:17:06 - 00:28:20:08
And event data recorders have you have
00:28:20:14 - 00:28:24:10
however long you were recording.
00:28:24:24 - 00:28:26:20
But there was a limitation to this.
00:28:26:20 - 00:28:29:20
I noticed that it was only working
on concrete surfaces.
00:28:30:02 - 00:28:33:05
And that's when I realized it has to do with
00:28:33:05 - 00:28:36:13
not only the tire characteristics,
but also the roadway surface.
00:28:37:14 - 00:28:40:27
So we made great advancements.
00:28:42:11 - 00:28:44:02
And that's when I realized
00:28:44:02 - 00:28:47:18
we have to do testing in commercial
vehicle tires to see if, in fact,
00:28:48:19 - 00:28:49:23
we have the same results.
00:28:49:23 - 00:28:52:23
And I will definitely go into that. But
00:28:54:01 - 00:28:57:21
in order to finish up this paper,
I had, Tony Cornetto
00:28:57:21 - 00:29:01:16
out on the East Coast
to take measurements with his vehicle,
00:29:02:04 - 00:29:05:18
and we ended up getting results
where we got slightly different results
00:29:05:27 - 00:29:09:21
because Tony's data, actually had harmonics,
00:29:09:22 - 00:29:13:14
which means you had the frequency of the rolling wheels,
00:29:13:14 - 00:29:16:14
but you had them in multiples of them stacked above.
00:29:17:03 - 00:29:19:06
And after
00:29:19:06 - 00:29:22:19
many, many months, I finally realized, what are the chances
00:29:23:06 - 00:29:25:22
that this has something to do with Tony's tires
00:29:25:22 - 00:29:28:01
That may be worn out?
00:29:28:01 - 00:29:31:01
So I reached out to him and asked them, are your tires
00:29:32:19 - 00:29:34:02
out of round?
00:29:34:02 - 00:29:35:05
And he said yes.
00:29:35:05 - 00:29:37:08
And then I asked, do your tires start...
00:29:37:08 - 00:29:38:02
Does your vehicle start
00:29:38:02 - 00:29:42:14
to vibrate at a specific frequency
of like a specific frequency?
00:29:42:16 - 00:29:43:21
And he said, yes.
00:29:43:21 - 00:29:47:08
And that's when we realized
that audio analysis was very useful
00:29:47:08 - 00:29:51:20
in helping us understand
the characteristics not only of the speed,
00:29:51:20 - 00:29:55:08
but also of the tires in question
that are actually being used.
00:29:56:18 - 00:29:58:01
So when
00:29:58:01 - 00:30:01:21
I asked the guys in California
to take measurements, and we couldn't find
00:30:01:21 - 00:30:05:19
a single measurement that was recorded
in California's concrete roadway,
00:30:06:11 - 00:30:10:28
and then we realized California
has shredded tires that are grounded
00:30:10:28 - 00:30:15:25
into the concrete mixtures
for the purposes of dampening the sound.
00:30:16:03 - 00:30:17:02
Right, right.
00:30:17:02 - 00:30:20:05
But that isn't
that isn't the case across all California,
00:30:20:05 - 00:30:23:05
because when I was out there
giving a presentation for cars,
00:30:23:11 - 00:30:26:08
I took, some measurements and
00:30:26:08 - 00:30:28:29
I got the frequency trace that I needed.
00:30:28:29 - 00:30:32:15
So I'm under the impression that maybe it has to be,
00:30:33:28 - 00:30:36:18
concentrated to a certain area in California
00:30:36:18 - 00:30:39:18
where shredded tires are used to,
00:30:39:27 - 00:30:41:25
dampen the sound. So.
00:30:41:25 - 00:30:48:05
So what is the minimum information that
you need in order to calculate the speed?
00:30:48:05 - 00:30:49:04
So you need to know the vehicle.
00:30:49:04 - 00:30:50:19
You need to know the model tire.
00:30:50:19 - 00:30:52:05
You need to
what do you need to know? No.
00:30:52:05 - 00:30:54:13
Interestingly enough,
if you look at the equation
00:30:54:13 - 00:30:56:10
and this is something I was mentioning
when I was at home
00:30:56:10 - 00:30:57:12
and when I was presenting,
00:30:57:12 - 00:31:01:10
this, method out in California for cars
in northern and Southern California.
00:31:01:24 - 00:31:05:21
I had mentioned, does it matter
what you're in making model of a vehicle?
00:31:06:18 - 00:31:09:13
And I got silence
and I said, let's look at the equation.
00:31:09:13 - 00:31:12:13
Where in the equation
does it tell you to enter the
00:31:12:13 - 00:31:15:18
the you're
making model of the, of the vehicle?
00:31:15:21 - 00:31:16:21
It doesn't.
00:31:16:21 - 00:31:21:29
The only thing that equation is asking you
is the frequency, the circumference
00:31:22:27 - 00:31:25:27
and the number of tire blocks
for that tire.
00:31:26:00 - 00:31:27:10
That's all you need.
00:31:27:10 - 00:31:30:06
So if that vehicle is no longer available,
00:31:30:06 - 00:31:33:06
I would highly suggest that you obtain
exemplary tires
00:31:33:06 - 00:31:36:24
and place them on a vehicle
and test it over that roadway.
00:31:36:24 - 00:31:38:03
Now, I'm sure that some attorneys
00:31:38:03 - 00:31:39:21
are going to say,
well, it's not the same vehicle,
00:31:39:21 - 00:31:43:12
although in that case, get one
that has that same making model,
00:31:43:16 - 00:31:47:02
but it's not going to make a difference
because it doesn't matter
00:31:47:06 - 00:31:51:16
what type of vehicle is being used,
it only matters what type of tire.
00:31:51:22 - 00:31:54:15
It's a tire. It's the rotation of the tire
and the contact to the road.
00:31:54:15 - 00:31:55:12
That makes sense.
00:31:55:12 - 00:31:58:26
So, do you know how.
00:31:58:27 - 00:32:00:25
So, for example, if you have a,
00:32:01:26 - 00:32:02:19
let's say, a semi
00:32:02:19 - 00:32:07:07
worn tire versus a brand new tire
and they're sort of moving
00:32:07:07 - 00:32:11:01
at the same speed, do you is there
00:32:11:01 - 00:32:14:01
is there a lot of sensitivity to error?
00:32:14:11 - 00:32:17:28
Well, if you look at the paper,
the second paper,
00:32:18:00 - 00:32:20:14
you'll notice that, Justin had,
00:32:20:14 - 00:32:23:18
I had actually zoomed in
to a section of the frequency,
00:32:24:06 - 00:32:28:01
and you can actually see that
if you chose the upper
00:32:28:01 - 00:32:31:29
band of the frequency trace versus
the lower, band of the frequency trace
00:32:31:29 - 00:32:36:20
you're looking at,
if I recall, a spread of 1.5.
00:32:36:28 - 00:32:38:09
Would you mind pulling that up?
00:32:38:09 - 00:32:39:18
Do you remember where that is?
00:32:39:18 - 00:32:41:18
Is it further down here? Let me see.
00:32:41:18 - 00:32:43:10
You might have to help me out here. Sure.
00:32:43:10 - 00:32:45:04
Oh, it's not on the screen right now.
00:32:45:04 - 00:32:47:11
Oh it's not, and let me bring that up.
Sorry about that.
00:32:47:11 - 00:32:50:09
Cloverleaf interchange roadway.
00:32:50:09 - 00:32:53:16
Let me see if I got it here. Go down more
00:32:55:00 - 00:32:58:00
a little more.
00:32:58:03 - 00:32:59:27
Now we're in just to the left.
00:32:59:27 - 00:33:01:04
It must be further up.
00:33:01:04 - 00:33:02:28
Okay, let me go back up.
I saw something here.
00:33:02:28 - 00:33:03:27
Let me see if I can bring it up.
00:33:03:27 - 00:33:04:26
I saw this.
00:33:04:26 - 00:33:07:19
Oh, there we go. There you go. Figure 11.
00:33:07:19 - 00:33:10:15
Figure 11 shows that,
00:33:10:15 - 00:33:11:17
the frequency trace.
00:33:11:17 - 00:33:14:21
When you zoom up to it,
you can either choose the upper
00:33:14:24 - 00:33:19:28
or the lower, portion,
and it's a spread between a 1.6mph.
00:33:20:05 - 00:33:23:21
So when we were doing our analysis,
we were selecting the,
00:33:24:09 - 00:33:27:23
the midpoint,
the median of that frequency trace. Now,
00:33:28:27 - 00:33:32:02
we noticed that that the thickness,
that trace
00:33:32:02 - 00:33:35:02
actually increases and decreases
based on,
00:33:36:20 - 00:33:37:20
tires being out of round.
00:33:37:20 - 00:33:41:09
So I imagine that not every single view,
every single tire is rotating
00:33:41:09 - 00:33:44:09
at the same frequency
or has the same roundness.
00:33:44:11 - 00:33:47:15
And as a result of that,
that frequency trace actually increases
00:33:47:27 - 00:33:51:16
as a result of,
anomalies on the tire itself.
00:33:52:02 - 00:33:55:23
And that's something we noticed with,
Tony's, testing out on the East Coast.
00:33:56:18 - 00:33:59:27
And I was, I was glad that we had that
00:34:00:13 - 00:34:04:21
because we ended up learning far
more than we initially thought.
00:34:04:22 - 00:34:10:03
So now we know how to address,
when we see various frequency traces, we
00:34:10:04 - 00:34:14:06
we can come to the conclusion
that those tires must be at around.
00:34:14:18 - 00:34:15:04
Yeah.
00:34:15:04 - 00:34:18:23
Hey, you sent me a few samples of audio,
and I don't know if this is a good time to
00:34:18:23 - 00:34:20:15
to play some of these, but,
00:34:20:15 - 00:34:24:27
you had, some of the vehicle stuff
you had, like, this motorcycle collision.
00:34:24:27 - 00:34:26:23
You got the, tractor.
00:34:26:23 - 00:34:30:06
You got this,
you said one that came from, that,
00:34:30:26 - 00:34:32:21
I don't know if it's a Mustang
or something like that.
00:34:32:21 - 00:34:35:11
And a doorbell.
We also have the firearm was one.
00:34:35:11 - 00:34:36:28
I think we'll all hold off on that one
00:34:36:28 - 00:34:41:05
until just for sure,
but is it one of these, like, yes.
00:34:41:05 - 00:34:45:10
For example, I found another way
of actually calculating speed.
00:34:45:12 - 00:34:51:03
If you know, the, the, the,
the frequency of the getting tired.
00:34:51:12 - 00:34:54:14
And that's where I believe
that's where we first met.
00:34:54:14 - 00:34:57:11
I found you in the hallway,
and I was like, hi, I'm Henry Vega.
00:34:57:11 - 00:34:59:08
And I want to introduce myself.
00:34:59:08 - 00:35:03:12
And I was there for, WREX
and I was part of the pedestrian
00:35:03:25 - 00:35:07:07
and crash team where a pedestrian
was being impacted by Mustang.
00:35:07:18 - 00:35:11:19
So I ended up recording with my mobile
phone, the collision itself.
00:35:11:19 - 00:35:15:15
And then I went back and I actually looked
at the audio and realized that
00:35:15:23 - 00:35:20:04
the skidding tires produce
a very definitive, frequency.
00:35:20:13 - 00:35:24:12
And when you take into account
the timing of the frequency and what kind
00:35:24:12 - 00:35:28:07
of frequency, you can actually calculate
what the speed is at various points.
00:35:28:07 - 00:35:32:00
So this is another method different
than the method that we had published.
00:35:32:27 - 00:35:35:09
And let me, let me, let me play this here
just a couple of times
00:35:35:09 - 00:35:36:08
where people can hear this.
00:35:36:08 - 00:35:37:17
And fortunately it's audio.
00:35:37:17 - 00:35:39:28
So if you're listening to the podcast
this will work for you.
00:35:39:28 - 00:35:40:22
So let's let's play this,
00:36:03:12 - 00:36:05:07
So in that audio clip,
00:36:05:07 - 00:36:08:07
we have a Mustang that's being accelerated.
00:36:08:28 - 00:36:13:17
And then it's impacting a, a pedestrian.
00:36:14:08 - 00:36:17:27
Well, a well, a Rescue Randy, that's being held together
00:36:18:15 - 00:36:21:15
or, over a, with structure.
00:36:21:21 - 00:36:25:22
And then after impact, the driver
applies, the brakes on the vehicle,
00:36:25:22 - 00:36:28:18
and that's where you have the skidding,
and we know where the vehicle was came
00:36:28:18 - 00:36:32:26
the rest, over the vehicle
came to rest along with the, pedestrian.
00:36:33:16 - 00:36:35:13
And then itself.
00:36:35:13 - 00:36:38:08
You can actually calculate speed
using the traditional methods,
00:36:38:08 - 00:36:40:03
but if you have the audio,
00:36:40:03 - 00:36:43:15
you know exactly when the impact
took place, which is time zero.
00:36:43:24 - 00:36:45:18
And then you know,
when it's getting takes,
00:36:45:18 - 00:36:47:03
how long it takes or how long after,
00:36:47:03 - 00:36:50:05
and you can actually work your way back
and you can actually calculate the speed.
00:36:51:21 - 00:36:53:26
This method
00:36:53:26 - 00:36:57:23
is something I haven't seen,
using the audio portion
00:36:57:23 - 00:37:00:23
for the purposes of reconstruction,
using the skidding.
00:37:01:09 - 00:37:03:25
But what about the motorcycle collision?
00:37:03:25 - 00:37:04:21
I'll play that.
00:37:04:21 - 00:37:07:25
But maybe you can preface this with
a description of what's happening there.
00:37:08:02 - 00:37:11:24
In that case, there was a surveillance camera
00:37:11:24 - 00:37:16:06
that was angled, and noticed two motorcycles going by.
00:37:16:06 - 00:37:18:16
The first motorcycle was traveling speed limit.
00:37:18:16 - 00:37:19:11
The second motorcycle
00:37:19:11 - 00:37:22:28
was obviously not traveling speed limit
because it was moving faster.
00:37:22:29 - 00:37:28:03
But if you look at the audio track,
you can definitely compare the signatures
00:37:28:03 - 00:37:31:03
and you can say what is obviously
traveling faster than the other.
00:37:31:04 - 00:37:34:21
But other than that, we were using
the Doppler effect to calculate speed.
00:37:35:00 - 00:37:38:27
Not only that,
if you know the distance of a skidmark
00:37:38:27 - 00:37:40:22
and you know the timing of the skidmark
00:37:40:22 - 00:37:44:01
and you know where the frequency,
what frequency to actually look for,
00:37:44:20 - 00:37:47:24
I was able to calculate the speed,
which was much higher than was
00:37:47:28 - 00:37:50:06
initially calculated
because the individuals
00:37:50:06 - 00:37:53:06
who calculated the speed
for the motorcycle in that case
00:37:53:16 - 00:37:57:24
were limited by the limitations
of the equations using stiffness,
00:37:58:17 - 00:38:02:05
which they they were really doing it
because it was a police vehicle
00:38:02:15 - 00:38:06:04
that had a cage in it,
which therefore increased the stiffness.
00:38:06:04 - 00:38:08:11
And that type of vehicle was not tested.
00:38:08:11 - 00:38:12:05
So therefore they knew their limitations,
and they calculated to about 71
00:38:12:05 - 00:38:12:24
miles an hour.
00:38:12:24 - 00:38:15:24
But if you take into account
the Doppler effect and the skidding,
00:38:16:00 - 00:38:20:03
the timing, the skinning, the speed
was much higher than 71 miles an hour.
00:38:20:04 - 00:38:21:12
Okay. Let me let me play this here.
00:38:21:12 - 00:38:23:27
I'm going to boost the volume a bit
and it's obviously come through well.
00:38:32:15 - 00:38:35:15
And again.
00:38:41:18 - 00:38:43:17
So it's that little sound
that we're hearing.
00:38:43:17 - 00:38:44:06
Yeah. Yes.
00:38:44:06 - 00:38:45:06
There were two sounds.
00:38:45:06 - 00:38:48:06
So the first one is, the first vehicle.
00:38:49:06 - 00:38:50:02
Traveling speed limit.
00:38:50:02 - 00:38:53:02
And the second one is not.
00:38:58:01 - 00:38:58:13
Okay.
00:38:58:13 - 00:39:01:13
Yeah, I hear it, I hear it. Cool.
00:39:01:17 - 00:39:03:08
You've got another one here.
00:39:03:08 - 00:39:04:17
It says doorbell.
00:39:04:17 - 00:39:06:12
So what's the doorbell? One.
00:39:06:12 - 00:39:09:20
That one is a surveillance video
of a nighttime
00:39:09:20 - 00:39:12:20
collision of a heavy vehicle
and a passenger vehicle.
00:39:13:00 - 00:39:15:24
This is classic,
because this is one of the main reasons I.
00:39:15:24 - 00:39:18:00
I wanted to write the first.
00:39:18:00 - 00:39:21:10
The first, the first paper,
which was if you have video,
00:39:21:20 - 00:39:25:11
you can and if you have ideal conditions,
you can actually calculate the speed.
00:39:25:11 - 00:39:28:20
But what if you don't have a line of sight
00:39:28:20 - 00:39:31:20
of the collision in question,
but all you have is sound.
00:39:31:27 - 00:39:34:00
And this is exactly what we have here.
00:39:34:00 - 00:39:39:09
So what we have is a tractor
vehicle making a right hand turn.
00:39:40:11 - 00:39:42:20
And then you have a passenger vehicle,
00:39:42:20 - 00:39:45:20
approaching and rear
impacting the heavy vehicle.
00:39:46:10 - 00:39:48:19
When you look at the video,
there's nothing that you can
00:39:48:19 - 00:39:51:21
actually, determine
based on the limitations.
00:39:52:00 - 00:39:55:00
But if you look at the audio, you know,
00:39:55:08 - 00:39:58:00
when the skidding took place, when the impact took place.
00:39:58:00 - 00:40:00:15
But also this is even far more interesting.
00:40:00:15 - 00:40:03:24
You find the second impact, the secondary impact.
00:40:03:26 - 00:40:05:13
Post collision.
00:40:05:13 - 00:40:09:15
So now you can actually calculate the,
the, the, post collision speed.
00:40:09:16 - 00:40:12:15
If you know the distance
between those two objects.
00:40:12:15 - 00:40:17:06
And I thought it was fascinating
because most people what I just said
00:40:17:24 - 00:40:20:27
we can't see the collision
and that isn't true.
00:40:20:29 - 00:40:24:26
And people internationally
have reached out to me and told me
00:40:25:20 - 00:40:29:14
I have a case where we don't have
the collision recorded,
00:40:30:00 - 00:40:33:23
but the audio, and I've read your paper
and I was wondering if maybe
00:40:33:23 - 00:40:37:11
you could take some time and explain to me
whether or not this is an ideal case.
00:40:38:02 - 00:40:39:22
Okay, let's play this one. Here we go.
00:40:51:06 - 00:40:52:01
So the
00:40:52:01 - 00:40:55:09
first impact was much louder in intensity.
00:40:55:09 - 00:40:56:16
And then you had the second impact,
00:40:56:16 - 00:40:59:11
which was secondary and it was lower in intensity.
00:40:59:11 - 00:41:04:12
So once again, if you have those details,
you can actually determine, pulse
00:41:04:12 - 00:41:08:10
collision travel and actually when I was working
00:41:08:16 - 00:41:12:04
on this whole audio aspect, Tony Cornetto
once again reached out to me and said,
00:41:12:04 - 00:41:14:18
I have a case, I need your help on where we have
00:41:14:18 - 00:41:17:12
a motorcycle that impacted a passenger vehicle.
00:41:17:12 - 00:41:20:09
And I told him, this is what's getting started.
00:41:20:09 - 00:41:23:19
This is the speed and there's a secondary impact.
00:41:23:19 - 00:41:25:21
I don't know what it is.
00:41:25:21 - 00:41:27:10
And that's when he told me, yes, there is.
00:41:27:10 - 00:41:29:08
This is the speed I calculated.
00:41:29:08 - 00:41:31:25
This is what the secondary impact is.
00:41:31:25 - 00:41:37:08
And, you can tell gear shifting on the
motorcycle and downshifting
00:41:38:11 - 00:41:39:08
once again.
00:41:39:08 - 00:41:43:03
Every time we look into an audio case,
there's more information that we
00:41:43:03 - 00:41:46:03
normally would have just over overlooked.
00:41:46:05 - 00:41:49:05
And that's what I ended up meeting.
00:41:49:16 - 00:41:52:22
Alan Asay, when I was at, Congress in,
00:41:53:25 - 00:41:55:13
for the SAE presentation.
00:41:55:13 - 00:41:59:00
WREX, he had told me
we haven't been listening to the evidence,
00:41:59:00 - 00:42:02:17
and he couldn't be further from the truth,
because have we know
00:42:02:18 - 00:42:04:01
we've been looking at the evidence,
00:42:04:01 - 00:42:07:01
but have we been looking at the others
and the answers?
00:42:07:13 - 00:42:08:29
No, we have it.
00:42:08:29 - 00:42:10:17
All right. Really good point.
00:42:10:17 - 00:42:11:07
Yeah.
00:42:11:07 - 00:42:13:04
Looking and listening are two different things.
00:42:13:04 - 00:42:17:24
So, let me switch over or ask you about firearms.
00:42:17:24 - 00:42:18:10
Now let's move
00:42:18:10 - 00:42:22:06
over into this particular area
because we did a little bit of work there.
00:42:22:06 - 00:42:23:09
And I think you've got,
00:42:23:09 - 00:42:27:27
we've got a sample of, of one of the,
the videos that we recorded.
00:42:27:27 - 00:42:30:09
I think that's what,
one of the things you gave me here.
00:42:30:09 - 00:42:34:11
So, if you want to maybe, maybe let's talk about
00:42:34:11 - 00:42:38:00
I mean, you started
with, at the beginning of the talk about,
00:42:38:00 - 00:42:41:08
you know, this this case you had
with shooting and firearms and then,
00:42:41:13 - 00:42:44:06
you know. So there's things that you can determine,
00:42:44:06 - 00:42:46:12
like how many shots are fired
and things like that.
00:42:46:12 - 00:42:48:00
But there's actually more of that
sitting there.
00:42:48:00 - 00:42:49:10
And I think that's
one of the things we found.
00:42:49:10 - 00:42:50:19
So help me out here.
00:42:50:19 - 00:42:54:15
What kinds of things can we determine
from the audio from a body camera or a
00:42:54:15 - 00:42:57:15
by standard, phone or something like that?
00:42:57:19 - 00:43:01:10
Well, sound is highly dependent on where the sound originates
00:43:01:10 - 00:43:02:14
from in the environment.
00:43:02:14 - 00:43:03:11
That's it.
00:43:03:11 - 00:43:06:11
And if you you have to say that
a couple times, I totally understand.
00:43:06:18 - 00:43:11:19
So if you if you create a sound,
that sound is going to be different,
00:43:13:12 - 00:43:16:27
where it's actually produced, whether it's
a hallway or whether it's an open room,
00:43:16:27 - 00:43:20:14
whether it's a room with carpeting,
or with a room without that carpeting.
00:43:20:27 - 00:43:22:07
And this is something that I,
00:43:22:07 - 00:43:25:18
I was pursuing when,
when we ended up doing research together,
00:43:26:16 - 00:43:30:01
I ended up taking the body camera footage,
00:43:30:04 - 00:43:33:20
from, from your testing
that you can look it up in Canada.
00:43:34:06 - 00:43:38:07
And I was able to find a detail
that I had never considered before.
00:43:38:18 - 00:43:40:23
For example,
00:43:40:23 - 00:43:41:26
a discharge.
00:43:41:26 - 00:43:44:26
Granted, we could find out
different calibers if we end up doing,
00:43:45:16 - 00:43:49:22
some pretty complex mathematics, on it,
instead of just looking at,
00:43:49:22 - 00:43:53:12
the suspect program,
it's a whole different, topic in itself.
00:43:54:05 - 00:43:56:12
So it's not just about looking
at the spectrogram, it's
00:43:56:12 - 00:43:59:13
actually processing the information
in a completely different manner.
00:43:59:27 - 00:44:02:27
But aside from that, I noticed that
00:44:03:08 - 00:44:06:14
the casings falling on the ground produced
00:44:06:14 - 00:44:09:23
a very distinctive frequency signature.
00:44:10:08 - 00:44:14:10
And I realized that they even had
a frequency signature
00:44:14:21 - 00:44:18:15
unique in itself, even though you were
comparing to the same caliber.
00:44:18:15 - 00:44:22:04
So if you had a caliber
of one manufacturer, that same caliber
00:44:22:08 - 00:44:26:25
produced a very distinct, frequency
if it was a different manufacturer
00:44:26:25 - 00:44:27:26
of the same caliber.
00:44:27:26 - 00:44:31:23
And I thought, this is an amazing aspect,
because I can imagine
00:44:31:23 - 00:44:35:29
that coming up in the future,
the casing falling on the ground
00:44:36:14 - 00:44:39:10
would create the frequency
associated with that caliber.
00:44:39:10 - 00:44:42:10
But not only that,
but also with the manufacturing question.
00:44:42:12 - 00:44:44:00
Yeah. Let me let me preface this.
00:44:44:00 - 00:44:46:02
So the the testing that we did,
00:44:46:02 - 00:44:49:13
so I ran the testing up here
and then I sent you all the footage.
00:44:49:21 - 00:44:53:09
So, just so people understand,
we have a, a shooter,
00:44:53:16 - 00:44:57:14
and, that shooter is surrounded
by a grid of cameras.
00:44:57:14 - 00:45:00:09
So we were using some axon body,
three cameras.
00:45:00:09 - 00:45:01:24
I believe we had five of them.
00:45:01:24 - 00:45:04:24
And then we had a couple of GoPros
as well, with some GoPros out there
00:45:04:24 - 00:45:08:12
in sort of different arrangements
at different distances from the shooter.
00:45:08:18 - 00:45:11:18
So let me play the let me play the fired
shot.
00:45:11:22 - 00:45:13:15
And again, I'm going to crank this up.
00:45:13:15 - 00:45:15:14
So be careful of what you're listening.
00:45:15:14 - 00:45:17:29
Obviously blow people's ears off.
So let's play this shot first.
00:45:26:07 - 00:45:26:16
Okay.
00:45:26:16 - 00:45:27:18
So that's the fired shot.
00:45:27:18 - 00:45:28:19
And we did run.
00:45:28:19 - 00:45:32:10
We ran different firearms,
like with different calibers.
00:45:32:10 - 00:45:36:05
And then we ran the same firearm
with different ammunition.
00:45:36:16 - 00:45:38:15
So we did we
we tried a couple of different things.
00:45:38:15 - 00:45:39:22
So that was that was the shot.
00:45:39:22 - 00:45:42:22
I only play it one more time.
00:45:47:03 - 00:45:47:16
Okay.
00:45:47:16 - 00:45:51:18
And then what we're going to do is so now
so what did you do.
00:45:52:01 - 00:45:55:24
Well isolate for the cartridge case
because it may be a little hard to hear.
00:45:55:24 - 00:45:57:06
It's there in the background.
00:45:57:06 - 00:45:59:16
But what did you do to isolate for this.
00:45:59:16 - 00:46:04:15
So then I identified the, the gunshot which is denoted
00:46:04:15 - 00:46:08:12
in a sharp increase in, frequencies,
which is known as the impulse.
00:46:08:28 - 00:46:13:08
And then I isolated the frequencies
00:46:13:08 - 00:46:16:18
that, had a very distinct bouncing action.
00:46:17:08 - 00:46:18:28
And I played that.
00:46:18:28 - 00:46:21:02
And that's when we ended up isolating
00:46:21:02 - 00:46:24:27
just the casing strapping,
which I guess you will play next.
00:46:25:01 - 00:46:26:00
Yes. Let's play this.
00:46:35:08 - 00:46:35:15
Yeah.
00:46:35:15 - 00:46:39:01
So, I mean, and this makes a lot of sense,
like if you think about what I,
00:46:39:05 - 00:46:43:05
the example that I use was, you know,
the cartridge case is like a little organ
00:46:43:05 - 00:46:44:06
pipe. It's a little pipe.
00:46:44:06 - 00:46:48:21
And so it's, it's going to,
it's going to have a sort of a frequency
00:46:48:21 - 00:46:51:21
that it's going to vibrate at or whatever.
00:46:51:29 - 00:46:55:28
And, you know, if you're looking at
a, a 45 caliber versus a 22
00:46:55:28 - 00:46:59:00
or something like that,
then they naturally
00:46:59:00 - 00:47:02:19
will have just different, frequencies
that they're going to resonate at.
00:47:02:19 - 00:47:05:02
So I think that's really interesting.
00:47:05:02 - 00:47:08:11
So, would you be able, for example,
00:47:08:11 - 00:47:11:18
to tell the difference
between the cartridge cases of,
00:47:12:02 - 00:47:15:19
you know, two different shooters
with different ammunition, let's say?
00:47:16:06 - 00:47:19:27
Yes, under ideal situations
like, let's say we actually had a ground
00:47:20:05 - 00:47:23:05
that actually would allow the the ringing
00:47:23:08 - 00:47:26:03
of that casing making contact.
00:47:26:03 - 00:47:29:12
I can foresee the next stage in our
in our research, I would recommend
00:47:29:12 - 00:47:33:10
dropping two different cart, casings
and I'm starting to realize
00:47:33:10 - 00:47:36:17
that we don't even need
to discharge a firearm in the area.
00:47:36:17 - 00:47:37:22
We just simply have to just
00:47:39:05 - 00:47:41:04
flip a, casing
00:47:41:04 - 00:47:45:26
or place a spent cartridge in a firearm
and, correct the slide,
00:47:45:26 - 00:47:48:07
and that would eject it by doing so,
00:47:48:07 - 00:47:52:05
I believe we can actually pinpoint exactly
which one fell first.
00:47:52:22 - 00:47:57:08
And because we're dealing with audio
and audio is amazingly accurate in terms
00:47:57:08 - 00:48:00:27
of, we're dealing in hundreds,
thousands of a second
00:48:01:01 - 00:48:04:26
so we can come very close to, to delineate
00:48:04:26 - 00:48:08:27
between which casing fell first versus
which one did not.
00:48:09:09 - 00:48:11:28
Yeah. That's pretty amazing. I love it.
00:48:11:28 - 00:48:13:18
And I think there's a lot of opportunities
there.
00:48:13:18 - 00:48:17:25
So let's, let's talk about
let's talk about what's next.
00:48:17:25 - 00:48:18:05
Henry.
00:48:18:05 - 00:48:22:27
So like, you know, you gone out, well,
you were at the, i.t.
00:48:23:00 - 00:48:25:25
Conference in England like last week.
00:48:25:25 - 00:48:28:21
So maybe maybe talk about that
and what you were doing there last week.
00:48:28:21 - 00:48:32:01
And then I'd like to ask
you also like what are your next moves?
00:48:32:01 - 00:48:32:12
Like what
00:48:32:12 - 00:48:33:25
what is the next priority for you
00:48:33:25 - 00:48:36:25
in terms of research or advancements
in these areas?
00:48:37:05 - 00:48:42:12
Well, when I was out in the UK, it's
because I met, Mark
00:48:42:12 - 00:48:47:00
Crouch and stuff and cash
and I, they were interested in knowing how
00:48:47:00 - 00:48:51:01
this whole audio could work
in, collision reconstruction.
00:48:51:08 - 00:48:54:16
And once I explained to them, they said,
00:48:54:16 - 00:48:57:16
well, we're going to be doing
testing out in the UK.
00:48:57:19 - 00:48:59:02
You're welcome to come.
00:48:59:02 - 00:49:01:25
And I'm glad that I was given
the opportunity because I was able
00:49:01:25 - 00:49:06:17
to, place a GoPro as well as a road,
wireless microphone
00:49:06:17 - 00:49:10:11
inside the passenger compartment
of the vehicle that was being tested.
00:49:10:21 - 00:49:15:01
And then I also had an exterior, GoPro
00:49:16:00 - 00:49:16:24
and rode,
00:49:16:24 - 00:49:19:24
microphone and a stationary location.
00:49:19:26 - 00:49:23:01
Both systems had two way radios.
00:49:23:05 - 00:49:24:24
So if you think about it,
00:49:24:24 - 00:49:29:09
if I have a third
radio and I and I click the radio
00:49:29:19 - 00:49:33:06
to say test one, click that clicking
00:49:33:17 - 00:49:37:15
is a synchronization for the cameras
and the exterior and the interior.
00:49:37:28 - 00:49:40:09
And when I did that
I was able to synchronize,
00:49:40:09 - 00:49:43:04
the audios
of those four different channels.
00:49:43:04 - 00:49:45:10
And then I evaluated it
00:49:45:10 - 00:49:48:10
and realized
that there was a speed trace to the
00:49:48:27 - 00:49:52:06
there was data there, and there's data
that I never even considered before.
00:49:52:14 - 00:49:56:13
So that information is going to
be compiled and it's going to be processed
00:49:56:26 - 00:50:00:20
and shared with the UK
community, per our agreement,
00:50:00:20 - 00:50:04:25
because I was given the opportunity
to go out there and it's only fair
00:50:04:25 - 00:50:08:26
that I processes and share it
with, with, the community out there.
00:50:09:09 - 00:50:12:10
As a result,
I met I met individuals in law enforcement
00:50:12:10 - 00:50:15:20
out in the UK who were interested
in learning this audio method.
00:50:16:06 - 00:50:19:18
And I can see why,
because if you don't have video,
00:50:20:03 - 00:50:23:03
but you have audio,
you certainly have the opportunity
00:50:23:03 - 00:50:26:16
to make calculations in those cases.
00:50:27:07 - 00:50:28:25
When we were out doing crash testing,
00:50:30:02 - 00:50:32:06
pedestrian, crash test was tested,
00:50:32:06 - 00:50:35:19
poll tested,
as well as multi-vehicle collisions.
00:50:36:06 - 00:50:40:09
And the amount of information you can see
it is amazing.
00:50:40:09 - 00:50:42:26
And it almost looks like artwork
in my opinion.
00:50:42:26 - 00:50:45:05
I think I might even consider actually
00:50:45:05 - 00:50:46:29
printing out something
and hang it on my wall,
00:50:46:29 - 00:50:51:01
because I think this is an avenue
that has opened up a whole new horizon.
00:50:51:15 - 00:50:54:11
So with that, that's what I'm doing.
00:50:54:11 - 00:50:55:15
That's what I did.
00:50:55:15 - 00:50:57:01
But two weeks ago.
00:50:57:01 - 00:51:00:03
So to answer your question,
what other aspects am I going into
00:51:00:03 - 00:51:01:07
your next?
00:51:01:07 - 00:51:04:06
We ended up doing
testing with commercial vehicle tires,
00:51:04:06 - 00:51:08:27
and it turns out that the amount
of information with commercial vehicle
00:51:08:27 - 00:51:13:18
tires being produced in audio, far exceeds
far exceeds passenger vehicles.
00:51:13:18 - 00:51:14:01
In other words,
00:51:15:29 - 00:51:17:17
commercial tires
00:51:17:17 - 00:51:20:26
have a speed trace
regardless of on the roadway,
00:51:21:16 - 00:51:24:23
whether it's asphalt, whether it's
concrete, it's producing signature.
00:51:24:23 - 00:51:27:14
Whereas passenger vehicle tires being,
00:51:27:14 - 00:51:30:11
softer,
I imagine don't have the same effect.
00:51:30:11 - 00:51:33:08
And and it was just
when you pointed that out to me
00:51:33:08 - 00:51:36:10
because I told them I'm not understanding
why we're having signatures
00:51:36:24 - 00:51:39:22
in commercial vehicle tires
where we're not having passengers.
00:51:39:22 - 00:51:42:04
And his response was the stiffer.
00:51:42:04 - 00:51:43:02
And I thought, you're right.
00:51:43:02 - 00:51:45:28
It's like I can't see the forest
because of the trees.
00:51:45:28 - 00:51:49:06
It was blatantly obvious, but
I needed someone to point this out to me.
00:51:49:06 - 00:51:52:00
I guess I got tunnel vision.
00:51:52:00 - 00:51:53:13
Yeah, that's super interesting.
00:51:53:13 - 00:51:55:15
So that's something
you're going to be looking at to then?
00:51:55:15 - 00:51:56:03
Okay.
00:51:56:03 - 00:51:59:18
Well, I, I know that I'm going
to be bugging you about firearms again.
00:52:00:09 - 00:52:02:23
Yes. We yes, I am
00:52:02:23 - 00:52:05:23
very,
very interested in doing more research.
00:52:06:16 - 00:52:08:12
But there's one more research
I forgot to mention.
00:52:08:12 - 00:52:12:15
We're doing research with respect
where we're taking motorcycle,
00:52:13:15 - 00:52:14:18
tire data
00:52:14:18 - 00:52:17:07
and trying to determine speed that way.
00:52:17:07 - 00:52:21:02
I think there is an avenue
that I was not expecting.
00:52:21:03 - 00:52:23:05
That's something I need to sit down
and speak with
00:52:23:05 - 00:52:25:04
and talk about,
because I think there's an avenue
00:52:25:04 - 00:52:28:10
that needs to be explored
that I did not see coming.
00:52:29:06 - 00:52:30:04
But other than that,
00:52:30:04 - 00:52:33:05
I am also looking forward
to doing firearms testing with you again.
00:52:33:08 - 00:52:36:18
We've talked
about what we our next phase.
00:52:36:21 - 00:52:39:27
I think if you recall, I think
you were referring to them as phases.
00:52:41:00 - 00:52:46:01
And, and I and I, I like the process
that we should, test it out
00:52:46:01 - 00:52:51:00
and then have others see if there, if,
if the results could be replicated.
00:52:51:10 - 00:52:51:18
Yeah.
00:52:51:18 - 00:52:54:18
I think it would be interesting
to see if we can determine,
00:52:54:22 - 00:52:57:13
different firearms
discharge at the same time,
00:52:57:13 - 00:53:00:11
because that's the kind of cases
I worked out in the past.
00:53:00:11 - 00:53:02:05
And I because it's more realistic.
00:53:02:05 - 00:53:03:12
Usually when you have a shootout,
00:53:03:12 - 00:53:06:20
you have more than one firearm
being discharged.
00:53:07:01 - 00:53:07:24
Yeah.
00:53:07:24 - 00:53:08:11
Hey, Henry,
00:53:08:11 - 00:53:11:12
if somebody wants to get Ahold of you
or they have some questions or whatever,
00:53:11:12 - 00:53:13:27
I've got the, the company website up here,
but I'm.
00:53:13:27 - 00:53:15:16
I'm guessing
there's a contact window there,
00:53:15:16 - 00:53:16:27
and I think there's
I think you're on there.
00:53:16:27 - 00:53:21:16
There's, like, an about, page or something
that that shows your, your information.
00:53:21:16 - 00:53:21:21
Right.
00:53:21:21 - 00:53:26:09
So the DJ's forensics.com, and,
I guess people can go there.
00:53:26:09 - 00:53:27:13
And you're also on LinkedIn, right?
00:53:27:13 - 00:53:28:20
You're on social media, too.
00:53:28:20 - 00:53:30:18
Yes, I am okay. Great.
00:53:30:18 - 00:53:34:16
Look, thanks so much for for your time
and for everything.
00:53:34:16 - 00:53:38:15
I think it's, just an amazing opportunity
that you have here.
00:53:38:15 - 00:53:42:13
You've opened up a whole bunch of doors,
on this whole audio thing, and,
00:53:42:26 - 00:53:45:22
just just like the little that I've seen
00:53:45:22 - 00:53:48:22
with the firearms and some of the things
the papers you're doing.
00:53:48:27 - 00:53:52:24
It just sounds really, just promising,
like giving people
00:53:52:24 - 00:53:56:09
another window, another opportunity,
like to do different types of analysis.
00:53:56:09 - 00:53:59:19
It's like a secondary way
to validate calculations and things.
00:53:59:19 - 00:54:01:08
So, great job.
00:54:01:08 - 00:54:03:24
I love what you're doing.
And keep up the great work.
00:54:03:24 - 00:54:04:09
Thank you.
00:54:04:09 - 00:54:06:17
Thank you for having me. Present this.
00:54:06:17 - 00:54:07:14
Thank you.
00:54:07:14 - 00:54:08:04
All right. Hey.
00:54:08:04 - 00:54:10:26
Hang back. I'll come back.
Yeah. Within just a second.
00:54:13:03 - 00:54:14:06
All right, folks, that's it.
00:54:14:06 - 00:54:17:02
Another episode of Forensics Talks.
00:54:17:02 - 00:54:20:01
Really interesting what's going on there
in the whole audio analysis world?
00:54:20:01 - 00:54:23:13
And, you know, sometimes with audio
analysis, people are thinking
00:54:23:13 - 00:54:27:09
about making identifications or, you know,
is it this speaker or that speaker?
00:54:27:14 - 00:54:29:27
But this is a whole other realm here.
00:54:29:27 - 00:54:33:18
And so, hey, thanks to Henry again,
and I really appreciate his time.
00:54:33:26 - 00:54:36:01
So thanks to all of you for listening.
00:54:36:01 - 00:54:37:18
Happy Thursday. It's
00:54:37:18 - 00:54:40:18
this is going to be aired on a Thursday,
and we'll see you on the next one.
00:54:40:18 - 00:54:41:23
Take care everyone. Bye bye.
