A Processing of Time Domain Audio Signal Seperation Based on FastICA and Subspace Signal Enhancement

• Main Authors: Yu-Wei Lin, 林昱偉
• Other Authors: 王昭男
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/4gr5s7

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 106 === Audio separation has always been the goal of signal processing. If we can extract the signals we need from many audio sources, and have a wide range of applications in the future, this paper uses independent component analysis to signal the signals of unknown sound sources. Analysis to solve the problem of blind signal separation. Independent Component Analysis(ICA) is the common algorithm to solve Blind Source Separation(BSS) problem. By using iteration algorithm, ICA can estimate the most optical demixing matrix for mixed signal. Theoretically, ICA can separate each voice which is made by different source from measured signal which are mixed. However, using time domain ICA algorithm will cause time delay difference problem because the distance between the signal source and each sensor is different. Even though we can transform measured signals into frequency-domain by Fourier Transform and avoid the problem, the ambiguities of ICA will cause dilation problem and permutation problem. The topic of paper is adding pre-processing step for solving and time delay difference problem. In addition, we use subspace speech enhance as post-processing to optimize ICA result.