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Currently, I am trying to work with the Dataset UrbanSound8K to try some Audio classification. And I got stuck in the preprocessing step already.

Since the audios are of different lengths, like 4 seconds or 0.3 seconds, I found it impossible to directly pass into the whitening algorithms like PCA even after Feature Extraction, using mel-spectrogram/ MFCC.

So my question is what I can do under such circumstance. I was wondering about zero-padding at the end of the shorter sequence. But it seems not working and not going to yield a nice result.

I saw some people using MFCC and summarizing the MFCCs along the time-axis, like mean, variance, kurtosis, skewness.... I think that would work in this case but I just wonder if there are any other ways to do so.

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I suggest that you compare things that are similar and don't compare things that are not. You could proceed with a preliminary classification of "short", "medium", and "long" and do a second set of classifications afterward. Within the duration classes, perform as much stretching and padding you need.

Another possibility is to break up everything into the shortest segments, extract short duration features and then uses those as tokens in a sequence.

Audio classification has many pathologies such as having more than one class present in a window.

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If the signals were of the same duration but obtained using different sampling rates, so that they have different sample lengths, you can resample the shorter sequence with a higher sampling rate to match that of the longer sequence.

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