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If I had 1000 audio files where three people are independently saying an animal at the same time, there can be 9 independent labels of animals. What features should I select from the audio file, and how should I normalize them to build the most accurate SVM to classify the audio files? For reference, I am doing this project in Python,so specific code examples and libraries would be very helpful. Thanks! -Joe

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  • $\begingroup$ Why would you use an SVM for this (in 2023)? Sounds a bit like a school project. $\endgroup$
    – hendrik
    Dec 4, 2023 at 6:35

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there can be 9 independent labels of animals.

One assumes the three speakers are making uniform draws from {opossum, anteater, roadrunner} or some similar urn. In this case counting syllables (3) would be uninformative, but for a different vocabulary we may see different results. Simple techniques can isolate those three animals from one another.

For a less constrained input vocabulary, 50 ms or 20 ms MFCC windows would be a pretty typical feature to train on.

Find a pre-trained model to tweak, or train from scratch.

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