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Titlebook: Blind Speech Separation; Shoji Makino,Hiroshi Sawada,Te-Won Lee Book 2007 Springer Science+Business Media B.V. 2007 Independent Component

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發(fā)表于 2025-3-21 17:06:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Blind Speech Separation
影響因子2023Shoji Makino,Hiroshi Sawada,Te-Won Lee
視頻videohttp://file.papertrans.cn/190/189152/189152.mp4
發(fā)行地址cutting edge topic on blind source separation.top researchers from all over the world.tutorial in nature and in-depth treatment
學(xué)科分類Signals and Communication Technology
圖書封面Titlebook: Blind Speech Separation;  Shoji Makino,Hiroshi Sawada,Te-Won Lee Book 2007 Springer Science+Business Media B.V. 2007 Independent Component
影響因子We are surrounded by sounds. Such a noisy environment makes it di?cult to obtain desired speech and it is di?cult to converse comfortably there. This makes it important to be able to separate and extract a target speech signal from noisy observations for both man–machine and human–human communication. Blindsourceseparation(BSS)isanapproachforestimatingsourcesignals using only information about their mixtures observed in each input channel. The estimation is performed without possessing information on each source, such as its frequency characteristics and location, or on how the sources are mixed. The use of BSS in the development of comfortable acoustic com- nication channels between humans and machines is widely accepted. Some books have been published on BSS, independent component ana- sis (ICA), and related subjects. There, ICA-based BSS has been well studied in the statistics and information theory ?elds, for applications to a variety of disciplines including wireless communication and biomedicine. However, as speech and audio signal mixtures in a real reverberant environment are generally convolutive mixtures, they involve a structurally much more ch- lenging task than instant
Pindex Book 2007
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Theoretischer und empirischer Hintergrund,r using frequency-domain independent component analysis (FD-ICA). Here, instead of using a fixed time or frequency basis to solve the convolutive blind source separation problem we propose learning an adaptive spatial–temporal transform directly from the speech mixture. Most of the learnt space–time
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https://doi.org/10.1007/978-3-531-92374-1od is valid when sources are W-disjoint orthogonal, that is, when the supports of the windowed Fourier transform of the signals in the mixture are disjoint. For anechoic mixtures of attenuated and delayed sources, the method allows one to estimate the mixing parameters by clustering relative attenua
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Kerstin Rabenstein,Evelyn Podubrinn the first stage, the mixing system is estimated, for which we employ hierarchical clustering. Based on the estimated mixing system, the source signals are estimated in the second stage. The solution for the second stage utilizes the common assumption of independent and identically distributed sour
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