Pattern Recognition with Neural Network in C++
Pattern Recognition with Neural Network in C++
KDD-Based Approach to Musical Instrument Sound Recognition
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Application of Temporal Descriptors to Musical Instrument Sound Recognition
Journal of Intelligent Information Systems
Musical instrument identification based on F0-dependent multivariate normal distribution
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Musical instrument recognition using cepstral coefficients and temporal features
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Musical instrument timbres classification with spectral features
EURASIP Journal on Applied Signal Processing
Processing of musical data employing rough sets and artificial neural networks
Transactions on Rough Sets III
Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The instrument recognition system described in this paper classifies isolated monophonic musical instrument sounds using six features: cepstral coefficients, constant Q transform frequency spectrum, multidimensional scaling analysis trajectories, RMS amplitude envelope, spectral centroid and vibrato. Sounds from nineteen instruments of definite pitch, covering the note range C3-C6 and representing the major musical instrument families and subfamilies were used to test the system. Nearest neighbor classification was utilised and results were evaluated in terms of accuracy and reliability. Using the leave-one-out test strategy yielded an accuracy of 93% in instrument recognition, 97% in instrument family recognition, and 100% for sustain/impulsive instruments.