Iterative Phase Estimation for the Synthesis of Separated Sources From Single-Channel Mixtures

Gunawan, David; Sen, D.

doi:10.1109/lsp.2010.2042530

Cited by 76 publications

(92 citation statements)

References 6 publications

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“…In general, however, we are required to estimate the phase spectrum of the clean signal. Future work should be dedicated to estimate the phase of the target speech signal from the noisy signal (for example by using the recent phase estimation algorithms [17,18,19], and feed it back to combine with the proposed phase-aware MMSE amplitude estimator presented here.…”

Section: Resultsmentioning

confidence: 99%

“…We acknowledge that the pre-knowledge of clean phase spectrum is unrealistic and needs to be replaced by an estimate of the clean speech phase using phase estimation algorithms, e.g. [17,18,19]. In our future work, we aim at presenting the general solution for parameter estimation and phase estimation where we have no prior assumption for clean phase spectrum.…”

Section: Figmentioning

confidence: 99%

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On phase importance in parameter estimation in single-channel speech enhancement

Mowlaee

Saeidi

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we study the impact of exploiting the spectral phase information to further improve the speech quality of the single-channel speech enhancement algorithms. In particular, we focus on the two required steps in a typical single-channel speech enhancement system, namely: parameter estimation solved by a minimum mean square error (MMSE) estimator of the speech spectral amplitude, followed by signal reconstruction stage, where the observed noisy phase is often used. For the parameter estimation stage, in contrast to conventional Wiener filter, a new MMSE estimator is derived which takes into account the clean phase information as a prior information. In our experiments, we show that by including the phase information in the two steps, it is possible to improve the perceived signal quality of the enhanced signal significantly with respect to the methods that do not employ the phase information.Index Terms-Single-channel speech enhancement, phase prior, Wiener filter.

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Section: Resultsmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

On phase importance in parameter estimation in single-channel speech enhancement

Mowlaee

Saeidi

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…2 Given that linear [17] and nonlinear [18] underdetermined BSS problems are difficult, the single-mixture nonlinear BSS problem is exceptionally difficult. Even linear BSS from a single mixture is a highly illposed problem, and hard constraints must be imposed on the source signals to enable their separation [25][26][27][28][29][30][31][32][33][34][35]. To the best of our knowledge, there is no other method proposed for the nonlinear BSS from a single mixture.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms for single-mixture BSS first have to transform the single-to the pseudo multi-mixture BSS problem [25][26][27][28][29][30][31][32][33][34][35]. Subsequently, some existing multivariate algorithms are used to perform BSS.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed single-mixture nonlinear BSS algorithm differs from the existing single-mixture BSS algorithms in the following aspects: (i) algorithms [25][26][27][28][29][30][31][32][33][34][35] address the linear BSS problem, while the proposed method addresses the nonlinear BSS problem and (ii) the hard constraints imposed on the source signals by single-mixture BSS algorithms [25][26][27][28][29][30][31][32][33][34][35] do not apply to the pure component mass spectra that are of interest in this study. This statement is supported through the following analysis.…”

Section: Introductionmentioning

confidence: 99%

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Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

Kopriva

Jerić

Brkljačić

2015

Journal of Chemometrics

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The nonlinear, nonnegative single-mixture blind source separation (BSS) problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined BSS problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra; afterwards, the pure components are extracted.Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi-mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi-mixture into a high-dimensional reproducible kernel Hilbert space (RKHS). Under sparse probabilistic conditions that were previously imposed on sources, the single-mixture nonlinear problem is converted into an equivalent linear, multiple-mixture problem that consists of the original sources and their higher order monomials. These monomials are suppressed by robust principal component analysis, hard-, soft-and trimmed thresholding. Sparseness constrained nonnegative matrix factorizations in RKHS yield sets of separated components.Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of 8 dependent components from 1 nonlinear mixture. The method is further demonstrated on 3 nonlinear chemical reactions of peptide synthesis in which 25, 19 and 28 dependent analytes are extracted from 1 nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry-based non-targeted metabolic profiling, such as biomarker identification studies.

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Phase Processing for Single‐Channel Source Separation

Mowlaee

Mayer

Kulmer

et al. 2016

Single Channel Phase‐Aware Signal Processing in Speech Communication: Theory and Practice

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Iterative Phase Estimation for the Synthesis of Separated Sources From Single-Channel Mixtures

Cited by 76 publications

References 6 publications

On phase importance in parameter estimation in single-channel speech enhancement

On phase importance in parameter estimation in single-channel speech enhancement

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

Phase Processing for Single‐Channel Source Separation

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