Reduced-Rank Spectra and Minimum Entropy Priors for Generalized Sound Recognition

We propose a generalized sound recognition system that uses reduced-dimension log-spectral features and a minimum entropy hidden Markov model classifier. The proposed system addresses the major challenges of generalized sound recognition—namely, selecting robust acoustic features and finding models that perform well across diverse sound types.
To test the generality of the methods, we sought sound classes consisting of time-localized events, sequences, textures and mixed scenes. In other words, no assumptions on signal composition were imposed on the corpus.
Comparison between the proposed system and conventional maximum likelihood training showed that minimum entropy models yielded superior performance in a 20-class recognition experiment. The experiment tested discrimination between speech, non-speech utterances, environmental sounds, general sound effects, animal sounds, musical instruments and commercial music recordings.