Content-based methods for the management of digital music

The literature on content-based music retrieval has largely finessed acoustic issues by using MIDI format music. This paper however considers content-based classification and retrieval of a typical (MPEG layer III) digital music archive. Two statistical techniques are investigated and appraised. Gaussian Mixture Modelling performs well with an accuracy of 92% on a music classification task. A Tree-based Vector Quantization scheme offers marginally worse performance in a faster, scalable framework. Good results are also reported for music retrieval-bysimilarity using the same techniques. Mel-Frequency Cepstral Coefficients parameterize the audio well, though are slow to compute from the compressed domain. A new parameterization (MP3CEP), based on a partial decompression of MPEG layer III audio, is therefore proposed to facilitate music processing at user-interactive speeds. Overall, the techniques described herein provide useful tools in the management of a typical digital music library.