There are a few things to try. One is transcription

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mostakimvip04
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Joined: Sat Dec 21, 2024 7:20 am

There are a few things to try. One is transcription

Post by mostakimvip04 »

A lot can go wrong here. Storms can affect reception, packets can be lost or corrupted before they reach our servers. The result can be time shifts or missing content. But most of the time the data winds up sitting comfortably on our hard drives unscathed.

Step 2: searching television
Video is terrible when you’re trying to look for a specific piece of it. It’s slow, it’s heavy, it is far better suited for watching than for working with, but sometimes you need to find a way.

if you have a time-coded transcript you can do anything. Like create a text editor for video, or search for key phrases, like “I approve this message.”

The problem is that most television is not precisely transcribed. Closed telegram data captions are required for most U.S. TV programs, but not for advertisements. Shockingly, most political ads are not captioned. There are a few open source tools out there for automated transcript generation, but the results leave much to be desired.

Introducing audio fingerprinting
We use a free and open tool called audfprint to convert our audio files into audio fingerprints.

An audio fingerprint is a summarized version of an audio file, one that has removed everything except the most “interesting“ pieces of every few milliseconds. The trick is that the summaries are formed in a way that makes it easy to compare them, and because they are summaries, the resulting fingerprint is a lot smaller and faster to work with than the original.

The audio fingerprints we use are based on a thing called frequency. Sounds are made up of waves, and each wave repeats–oscillates–at different rates. Faster repetitions are linked to higher sounds, lower repetitions are lower sounds.

An audio file contains instructions that tell a computer how to generate these waves. Audfprint breaks the audio files into tiny chunks (around 20 chunks per second) and runs a mathematical function on each fragment to identify the most prominent waves and their corresponding frequencies.

The rest is thrown out, the summaries are stored, and the result is an audio fingerprint.

If the same sound exists across two files, a common set of dominant frequencies will be seen in both fingerprints. Audfprint makes it possible to compare the chunks between two sound files, count how many they have in common, and how many appear in roughly the same distance from one another.
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