This matches my experience. In Kaggle audio competitions, I've seen many competitors struggle with basics like proper PCM filtering - anti-aliasing before downsampling, handling spectral leakage, etc.

Audio really is a blue ocean compared to text/image ML. The barriers aren't primarily compute or data - they're knowledge. You can't scale your way out of bad preprocessing or codec choices.

When 4 researchers can build Moshi from scratch in 6 months while big labs consider voice "solved," it shows we're still in a phase where domain expertise matters more than scale. There's an enormous opportunity here for teams who understand both ML and signal processing fundamentals.

Also, while the author complains that there is not a lot of high quality data around [0], you do not need a lot of data to train small models. Depending on the problem you are trying to solve, you can do a lot with single-digit gigabytes of audio data. See, e.g., https://jmvalin.ca/demo/rnnoise/

[0] Which I do agree with, particularly if you need it to be higher quality or labeled in a particular way: the Fisher database mentioned is narrowband and 8-bit mu-law quantized, and while there are timestamps, they are not accurate enough for millisecond-level active speech determination. It is also less than 6000 conversations totaling less than 1000 hours (x2 speakers, but each is silent over half the time, a fact that can also throw a wrench in some standard algorithms, like volume normalization). It is also English-only.

imo audio DSP experts are diametrically opposed to AI on moral grounds. Good luck hiring the good ones. It's like paying doctors to design guns.

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