Are MFCCs the optimal method of representing music to a retrieval system? A signal processing technique, the Mel frequency Cepstrum, is often used to extract information from a musical piece for use in a machine learning task.  This method gives a short-term power spectrum, and the coefficients are used as input.
In designing music retrieval systems, such coefficients are considered characteristic of a piece (obviously not necessarily unique, but distinguishing).  Are there any characteristics that would better suit learning with a network?  Would time-varying characteristics like the bass progression of the piece used in something like an Elman network work more effectively?  
Which characteristics would form an extensive enough set upon which classification could take place?
 A: We did a bit of work on this at one point. The set of features we extracted are given in this NIPS workshop paper. I have to admit we couldn't replicate the results of some other authors in the field, although there were some doubts about the datasets used in these (note that the datasets used by authors in this field tend to be hand-picked and not released to the public, for copyright reasons, although this not always the case). Essentially they were all short-term spectral features with Autoregression coefficients thrown in too. We were looking at classification of genre, which we know can be done by humans (although not with wonderful accuracy, and not with consistent agreement ....) in very short timespans (<1s), which validates the use of short term features. If you're interested in doing more complicated things than the typical genre/artist/album/producer classification then you might need more long-range features, otherwise these short-term spectral features tend to perform best.
