| Summary: | 碩士 === 大同大學 === 資訊工程學系(所) === 93 === Blind source separation is to find the underlying factors or components from the observed mixtures of unknown source signals. Independent Component Analysis(ICA), which is a statistical and computational technique, is used to fulfill blind source separation. Most ICA techniques are implemented on that the number of
mixtures is equal to or greater than the number of sources. In particular, we focus on separating two unknown sources from one mixture.
This thesis will introduce the background knowledge of ICA. It contains the introduction of some basic de‾nitions of ICA, the preprocessing technique needed for the acceleration of convergence, ICA by maximization of non-Gaussianity and ICA by maximum likelihood estimation. Besides, ICA in the frequency domain will also be described.
Then, we will describe the algorithms for single channel source identification and separation. It exploits a priori sets of time-domain basis functions learned by generalized Gaussian ICA for the sound sources given in the training set. The source identification is based on the Algebraic Matrix-Distance Index (AMDI) algorithm, and source separation is based on the maximum likelihood estimation.
Some experiments that identify the two musical instruments involved in a symphony and separate the sound coming from each of them are demonstrated to investigate the performance of the approach. Finally, we will address our future works and give some directions on future researches.
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