I am building an application that would "listen" to the microphone input, analyse it, and compare the analysis to a pre-analysed and pre-classified sound bank (small - maximum 20 sounds). It will then show the user what sound it was.
Now, I have a vague idea on how to implement this. I would like to choose a set of features that would best represent the sounds. The issue is that the sounds in the sound bank could be whatever the user recorded. From strong onsets and short sounds, to long onsets and long sounds.
The current features I'm thinking of are:
- Spectral Centroid
- Spectral Flux
- Spectral Rolloff
What do you think? Would these be sufficient to properly classify the sound? Also, as these features output a single value for specific sound buffer, how would you go about handling the feature vector that represents the whole sound? I am using kNN for classification, and was wondering what's the best way to compare two feature vectors? would cross-correlation be a feasible technique?
Thanks a lot!
P.S I have seen that a similar question was asked here, but it doesn't fully answer my issues.