I'm trying to detect and classify non-speech sounds. Currently, I'm using a series of moving overlapped power spectrums from training sounds as the features I am looking for.
When I do analysis, I'm just computing the same amount of overlapped spectrums so that the number of features are the same. Right now the performance is not very good, it can only detect silence vs non-silence.
What techniques are there for this type of signal detection? One of my concerns is that for sounds of different lengths in the time domain would result in different lengths of feature vectors which so I can't use the same classifier, I'm stuck on this.
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