Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Estimation of musical sound separation algorithm effectiveness employing neural networks
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Musical Instruments in Random Forest
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Monaural musical sound separation based on pitch and common amplitude modulation
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive harmonic spectral decomposition for multiple pitch estimation
IEEE Transactions on Audio, Speech, and Language Processing
Dynamic spectral envelope modeling for timbre analysis of musical instrument sounds
IEEE Transactions on Audio, Speech, and Language Processing
Journal of Intelligent Information Systems
Harmonic source separation using prestored spectra
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Temporal Integration for Audio Classification With Application to Musical Instrument Classification
IEEE Transactions on Audio, Speech, and Language Processing
Musical instrument recognition by pairwise classification strategies
IEEE Transactions on Audio, Speech, and Language Processing
A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering
IEEE Transactions on Audio, Speech, and Language Processing
Musical Instrument Classification Using Individual Partials
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we propose a method based on probabilistic mixture model decomposition that can simultaneously identify musical instrument types, estimate pitches and assign each pitch to its source instrument in monaural polyphonic audio containing multiple sources. In the proposed system, the probability density function (PDF) of the observed mixture note is treated as a weighted sum approximation of all possible note models. These note models, covering 14 instruments and all their possible pitches, describe their dynamic frequency envelopes in terms of probability. The weight coefficients, indicating the probabilities of the existence of pitches of a certain type of instrument, are estimated using the Expectation-Maximization (EM) algorithm. The weight coefficients are used to detect the types of source instruments and the pitches. The results of experiments involving 14 instruments within a designated pitch range F3---F6 (37 pitches) demonstrate a good discrimination capability, especially in instrument identification and instrument-pitch identification. For the entire system including the note onset detection tool, using quartet polyphonic recordings, the average F-measure values of instrument-pitch identification, instrument identification and pitch estimation were 55.4, 62.5 and 86 % respectively.