Paper #3

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Xiaoming Zhao, Qiang Tuo, Ruosi Guo and Tengteng Kong

Abstract: The isolation of mixed music signals is beneficial to the extraction and identification of music signal features and to enhance music signal quality. This paper briefly introduced the mathematical model for separating blind source from mixed music signals and the traditional Independent Component Analysis (ICA) algorithm. The separation algorithm was optimized by the complex neural network. The traditional and optimized ICA algorithms were simulated in MATLAB software. It was found that the time-domain waveform of the signal isolated by the improved ICA-based separation algorithm was closer to the source signal. The similarity coefficient matrix, signal-to-interference ratio, performance index, and iteration time of the improved ICA-based algorithm was 62.3, 0.0011, and 0.87 s, respectively, which were all superior to the traditional ICA algorithm. The novelty of this paper is setting the initial iterative matrix of the ICA algorithm with the complex neural network.

Keywords: Blind source separation; Complex neural network; Independent component analysis; Mixed music signal, Numerical filter, Short-time Fourier transform.