Title :
Automatic cymbal classification using non-negative matrix factorization
Author :
Cavaco, Sofia ; Almeida, Hugo
Author_Institution :
Dept. de Inf., Univ. Nova de Lisboa, Caparica, Portugal
Abstract :
Several musical instrument classifiers have been proposed. While many approaches in sound-feature extraction and in sound classification have been successfully used, most focus on distinguishing different harmonic instruments such as the violin and the flute, whose sounds have very different characteristics. On the other hand, much less attention has been given to percussion instruments, especially if we consider the discrimination of instruments of the same type, like the cymbals in a drum kit. Here, we propose a classifier that is able to distinguish this latter type of instruments. The classifier is able to distinguish sounds with very similar properties, like sounds produced by instruments with similar geometry that differ in material or size. In particular it is able to distinguish sounds from the cymbals in a drum kit. Instead of using a set of predefined features, the classifier learns spectral features from the data using non-negative matrix factorization. This work is important to fill the gap on percussion instrument classification and transcription (since most music transcribers focus on harmonic instruments).
Keywords :
acoustic signal processing; matrix decomposition; musical instruments; signal classification; automatic cymbal classification; drum kit; flute; harmonic instruments; musical instrument classifiers; nonnegative matrix factorization; percussion instrument classification-transcription; sound classification; sound-feature extraction; spectral features; violin; Computer crashes; Feature extraction; Instruments; Music; Spectrogram; Tin; Training; acoustic signal processing; indefinite pitch; non-negative matrix factorization; percussion instruments; sound classification;
Conference_Titel :
Systems, Signals and Image Processing (IWSSIP), 2012 19th International Conference on
Conference_Location :
Vienna
Print_ISBN :
978-1-4577-2191-5