Detection of a Specific Musical Instrument Note Playing in Polyphonic Mixtures by Extreme Learning Machine and Particle Swarm Optimization

摘要：

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.

关键词： Extreme Learning Machine Particle Swarm Optimization Musical Signal Processing Musical Information Retrieval

作者: Pat Taweewat

作者单位: School of Electrical and Information Engineering The University of Sydney NSW, Australia

会议类型: 国际会议

会议名称: 2010 International Conference on Future Information Technology(2010年未来信息技术国际会议 ICFIT 2010)

会议地点: 长沙

会议语种:英文

页码: 952-958

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）