user33801: I want to make a compilation video of Tesla FSD Beta's disengagements. When FSD Beta disengages, it makes a particular sound. I would like a piece of software I can use without having to code that will show me the timestamps of each time the sound is heard. Ideally, it would be a video editor or something similar which could also allow to trim the video. I would prefer if it was free or at least free trial.

user3592: You'd probably want to download them, and process the audio, so that you could search faster than realtime.  Doing that without a paid, premium subscription might violate YouTube's Terms of Service, and it might even violate the TOS WITH a premium subscription, so be sure you study those carefully, first.
If you can manage to download them, you might be able to use fast Fourier analysis to fingerprint the sound, and then search the files for it.  You don't necessarily need to know all of the details about the signal processing, as long as you can find a tool that'll help you do it.  I'd recommend having a look at this article as a starting point, and searching online code repositories like GitHub for something that suits your level of engagement and code familiarity.  Even if you don't have any experience coding, you can sometimes find links to helpful google colabs or jupyter notebooks installations.  Here's one I found one earlier today for music segmentation, that will analyze music from YouTube links and spit out Midi keyboard parts, just by clicking through the colab.
If you aren't able to download the files, you could still theoretically use software to analyze the stream of audio coming through your speakers, worst case, but you'll still need to find suitable tools to fingerprint it, and I'm less confident that you'd be able to find an off-the-shelf solution which works that way.

user20528: I'm not aware of any easy software to do that, but I'm posting anyway, since it could be useful to others as well.
There are open source projects that attempt to do what you require, basically by applying what @Jason Conrad said in his answer.
One of them is craigfrancis/audio-detect that you can find on GitHub. You first need to generate samples of the sound that you want to detect, and generally the more you have the better the performance of the algorithm will be. This can be done quite simply with the command that you find in the readme.
After that, the actual processing can be run, which takes the samples you extracted and tries to find instances of the same sound in an input file.
It's pretty easy to run both operations if you know a little about command line. Unfortunately, there's probably no magic solution for this task.

33801: <p>I want to make a compilation video of Tesla FSD Beta's disengagements. When FSD Beta disengages, it makes a particular sound. I would like a piece of software I can use without having to code that will show me the timestamps of each time the sound is heard. Ideally, it would be a video editor or something similar which could also allow to trim the video. I would prefer if it was free or at least free trial.</p>
 3592: <p>You'd probably <em>want</em> to download them, and process the audio, so that you could search faster than realtime.  Doing that without a paid, premium subscription might violate YouTube's Terms of Service, and it might even violate the TOS <em>WITH</em> a premium subscription, so be sure you study those carefully, first.</p>
<p>If you can manage to download them, you might be able to use fast Fourier analysis to fingerprint the sound, and then search the files for it.  You don't necessarily need to know all of the details about the signal processing, as long as you can find a tool that'll help you do it.  I'd recommend having a look at <a href="https://willdrevo.com/fingerprinting-and-audio-recognition-with-python/" rel="nofollow noreferrer">this article</a> as a starting point, and searching online code repositories like <a href="https://github.com" rel="nofollow noreferrer">GitHub</a> for something that suits your level of engagement and code familiarity.  Even if you don't have <em>any</em> experience coding, you can sometimes find links to helpful google colabs or jupyter notebooks installations.  <a href="https://bit.ly/OmnizartColab" rel="nofollow noreferrer">Here's one</a> I found one earlier today for music segmentation, that will analyze music from YouTube links and spit out Midi keyboard parts, just by clicking through the colab.</p>
<p>If you aren't able to download the files, you could still theoretically use software to analyze the stream of audio coming through your speakers, worst case, but you'll still need to find suitable tools to fingerprint it, and I'm less confident that you'd be able to find an off-the-shelf solution which works that way.</p>
 20528: <p>I'm not aware of any easy software to do that, but I'm posting anyway, since it could be useful to others as well.</p>
<p>There are open source projects that attempt to do what you require, basically by applying what @Jason Conrad said in his answer.</p>
<p>One of them is <a href="https://github.com/craigfrancis/audio-detect" rel="nofollow noreferrer">craigfrancis/audio-detect</a> that you can find on GitHub. You first need to generate samples of the sound that you want to detect, and generally the more you have the better the performance of the algorithm will be. This can be done quite simply with the command that you find in the readme.</p>
<p>After that, the actual processing can be run, which takes the samples you extracted and tries to find instances of the same sound in an input file.</p>
<p>It's pretty easy to run both operations if you know a little about command line. Unfortunately, there's probably no magic solution for this task.</p>
