Automatic Recognition of Isolated Monophonic Musical Instrument Sounds using kNNC

摘要

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.