Single-Channel Speech Separation Using Soft Mask Filtering

Radfar, Mohammad Hadi; Dansereau, Richard M.

doi:10.1109/tasl.2007.904233

Cited by 91 publications

(82 citation statements)

References 42 publications

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“…Performing a procedure similar to the one presented in [15], = , = , SSR can be approximated in the following form…”

Section: Estimating Ssr and Detecting The Subsourcesmentioning

confidence: 99%

“…This is in general an ill-posed problem and can not be solved without further knowledge about the sources or their interrelationship. Possible SCSS methods are mainly divided into two categories: source driven [4][5][6][7] and model-based methods [12][13][14][15][16][17][18]. As a major example for the first group, computational auditory scene analysis (CASA) has widely been studied [4].…”

Section: Introductionmentioning

confidence: 99%

“…This speaker dependent model is used as source prior knowledge and is applied for separation without considering the interfering component. The most prominent models are vector quantization (VQ) [12], [16], Gaussian mixture models (GMM) [14], [15] and Hidden Marcov models (HMM) [17]. In most recent proposed model-based SCSS techniques, it is assumed that the test speech files are recorded at a condition similar to that of the training phase recording.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Approach for Single Channel Speech Separation

Kammi¹,

Karami²

2012

IJMLC

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Abstract-This paper addresses the problem of single channel speech separation to extract and enhance the desired speech signals from mixed speech signals. We propose a new speech separation algorithm by utilizing Bayesian approach for the case in which, underlying sources are mixed at different levels of energies. This situation is not considered in many single channel speech separation methods. To validate the effectiveness of our proposed method, it is compared with a state-of-the art method which is a gain adapted Maximum Likelihood estimator. Through the experiments, we show that our proposed method outperforms the compared method.Index Terms-Single channel speech separation, bayesian approach, gain estimation.

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“…Performing a procedure similar to the one presented in [15], = , = , SSR can be approximated in the following form…”

Section: Estimating Ssr and Detecting The Subsourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Approach for Single Channel Speech Separation

Kammi¹,

Karami²

2012

IJMLC

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show abstract

“…Many techniques have been proposed to solve this problem. These approaches are mainly divided into two categories: source driven [1][2][3][4] and model-based methods [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…The most prominent models are vector quantization (VQ) [9], [13], Gaussian mixture models (GMM) [11], [12] and Hidden Marcov models (HMM) [14]. Given the individual speakers' models, an estimation technique is applied to estimate the sources.…”

Section: Introductionmentioning

confidence: 99%

Single Channel Speech Separation Using an Efficient Model-based Method

Kammi¹,

Karami

2015

IJITCS

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Abstract-The subject of extracting multiple speech signals from a single mixed recording, which is referred to single channel speech separation, has received considerable attention in recent years and many model-based techniques have been proposed. A major problem of most of these systems is their inability to deal with the situation in which the signals are combined at different levels of energies because they assume that the data used in the test and training phase have equal levels of energies, where, this assumption hardly occurs in reality. Our proposed method based on MIXMAX approximation and subsection vector quantization (VQ) is an attempt to overcome this limitation. The proposed technique is compared with a technique in which a gain adapted minimum mean square error estimator is derived to estimate the separated signals. Through experiments we show that our proposed method outperforms this method in terms of SNR results and also reduces computational complexity.

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A Survey on Single Channel Speech Separation

Logeshwari

Mala

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Single-Channel Speech Separation Using Soft Mask Filtering

Cited by 91 publications

References 42 publications

A Bayesian Approach for Single Channel Speech Separation

A Bayesian Approach for Single Channel Speech Separation

Single Channel Speech Separation Using an Efficient Model-based Method

A Survey on Single Channel Speech Separation

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