Abstract—In this work, we present a system for detecting a specific musical instrument note in polyphonic mixtures based on machine learning method. By using extreme learning machine as a classifier, we generate input features from magnitude data of FFT spectrum, which are processed by an adjustable dynamic level processor. We use the particle swarm optimization to tune the parameter values of this level processor to make the system have the optimal detection performance for the specific instrument signal. Three musical instruments were used in the experiments. These were trumpet, flute and clarinet. We also compare the detection results between using ELM with linear and non-linear output functions for selected features. Furthermore, we show that our system can be used as a musical instrument source tracking system by creating a trajectory of its fundamental frequency using the information from the outputs of the system and illustrating over the spectrogram.
Index Terms—Extreme learning machine, particle swarm optimization, musical signal processing; musical information retrieval
Pat Taweewat is with School of Electrical and Information Engineering the University of Sydney, NSW, Australia (e-mail:firstname.lastname@example.org)
Cite: Pat Taweewat, "Detection of a Specific Musical Instrument Note Playing in Polyphonic Mixtures by Extreme Learning Machine and Particle Swarm Optimization," International Journal of Information and Electronics Engineering vol. 2, no. 5, pp. 741-747, 2012.