A survey of semi-blind ICA for speech separation in frequency domain

Lin, Qiu‐Hua; Hao, Yingguang

doi:10.1109/icgcs.2010.5542985

Cited by 2 publications

(1 citation statement)

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“…Most of the existing BSS algorithms output all of the speeches by performing separation of convolutive mixtures in either time domain or frequency domain [2][3]. However, in many applications only a target signal, e.g., the speech of a person who was giving a lecture, or the speech of an important person, is of interest for speech enhancement.…”

Section: Introductionmentioning

confidence: 99%

A semi-blind negentropy maximization algorithm for enhancing a specific speech

Lin

Gong

2011

2011 Seventh International Conference on Natural Computation

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Extraction of a specific speech signal from convolutive mixtures of multiple speeches is a challenge since different speeches may share similar characteristics. Based on our semiblind negentropy maximization algorithm for separating multiple speech signals, we further present an algorithm for extracting a desired speech by constructing a corresponding reference signal. Specifically, two kinds of reference signals are explored, which include a clear speech from the specific speaker and a rough estimation of blind source separation, respectively. Extensive experiments with synthetic data and recorded speeches are carried out to test the performance. The results show that the proposed algorithm can nicely extract an expected speech signal but discard the other speeches.

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Section: Introductionmentioning

confidence: 99%

A semi-blind negentropy maximization algorithm for enhancing a specific speech

Lin

Gong

2011

2011 Seventh International Conference on Natural Computation

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Separation of Linearly Mixed Speech Signals using DWT based ICA

Singh¹,

Singh²

2013

IJCA

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Speech is the fundamental means of communication among humans. Speech production is the process of converting a linguistic message to the acoustic waveform. Separating various linearly mixed speech signals is often modelled by famous cocktail party problem and can be achieved by a technique known as Independent Component Analysis (ICA). ICA is similar to PCA and Factor analysis but it works on non-Gaussian mixture of signals. In this paper, the problem of separating linearly mixed signals is solved by using filter banks with ICA. Comparison of existing ICA technique with the one proposed is done based on experimental results which shows that the proposed algorithm over performs basic ICA.

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A survey of semi-blind ICA for speech separation in frequency domain

Cited by 2 publications

References 10 publications

A semi-blind negentropy maximization algorithm for enhancing a specific speech

A semi-blind negentropy maximization algorithm for enhancing a specific speech

Separation of Linearly Mixed Speech Signals using DWT based ICA

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