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首页> 外文会议>International Symposium on Neural Networks pt.1; 20040819-20040821; Dalian; CN >Speech Segregation Using Constrained ICA
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Speech Segregation Using Constrained ICA

机译：使用约束ICA进行语音隔离

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摘要

In natural environment, speech often occurs concurrently with acoustic interference. How to effectively extract speech remains a great challenge. This paper describes a novel constrained Independent Component Analysis (ICA) approach, the ICA with reference (ICA-R), to speech segregation. Different from the traditional ICA which recovers simultaneously all the source signals, the ICA-R extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. We show how the ICA-R can be applied to separate a target speech signal from interfering sounds by exploiting a proper reference signal, which is based on the different characteristic between speech signal and its environmental noises, i.e., the speech signal has pitch and its harmonic frequencies whereas the noises usually do not. Results of computer experiments demonstrate the efficiency of the proposed method.
机译：在自然环境中，语音通​​常与声音干扰同时发生。如何有效地提取语音仍然是一个巨大的挑战。本文介绍了一种新颖的约束独立成分分析（ICA）方法，即参考语音的ICA（ICA-R），用于语音分离。与同时恢复所有源信号的传统ICA不同，ICA-R通过将一些先验信息整合到分离过程中，仅从源信号的混合物中提取一些所需的源信号。我们展示了如何通过利用适当的参考信号将ICA-R应用于目标语音信号与干扰声音的分离，该参考信号基于语音信号及其环境噪声之间的不同特性，即语音信号具有音高和谐波频率，而噪声通常不会。计算机实验结果证明了该方法的有效性。

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