Title :
A missing feature approach to instrument identification in polyphonic music
Author :
Eggink, Jana ; Brown, Guy I.
Author_Institution :
Dept. of Comput. Sci., Univ. of Sheffield, UK
Abstract :
Gaussian mixture model (GMM) classifiers have been shown to give good instrument recognition performance for monophonic music played by a single instrument. However, many applications (such as automatic music transcription) require instrument identification from polyphonic, multi-instrumental recordings. We address this problem by incorporating ideas from missing feature theory into a GMM classifier. Specifically, frequency regions that are dominated by energy from an interfering tone are marked as unreliable and excluded from the classification process. This approach has been evaluated on random two-tone chords and an excerpt from a commercially available compact disc, with promising results.
Keywords :
Gaussian processes; audio signal processing; music; musical instruments; object recognition; signal classification; GMM classifier; Gaussian mixture model classifier; automatic music transcription; instrument identification; instrument recognition; missing feature theory; multi-instrumental recordings; polyphonic music; Acoustic waves; Application software; Audio recording; CD recording; Computer science; Frequency estimation; Instruments; Multiple signal classification; Robustness; Speech recognition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1200029