On single-channel noise reduction with rank-deficient noise correlation matrix

Pan, Ningning; Benesty, Jacob; Chen, Jingdong

doi:10.1016/j.apacoust.2017.05.010

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…• Correlation factor analysis: By standardising the original indicators do after using the correlation matrix (Pan et al, 2017) to detect the suitability and feasibility of Factor Analysis Model. The Correlation factor analysis results are shown in Table 3.…”

Section: Research On Key Indicators and Regional Comparison Of Green ...mentioning

confidence: 99%

Research on key indicators and regional comparison of green data centre

Mei

Feng

Wen

2021

IJRIS

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With the continuous growth of the amount of information, the scale of green data centre is becoming larger and larger, and it is more and more widely used, the construction of green index system of green data centre has become a key problem to be solved urgently. This paper studied on the basic index system of green data centre, with the help of PUE, CEI and TCO index analysis, it is made in depth analysis of the factors affecting the green index of the green data centre, factor analysis is used from nine aspects such as annual average temperature, annual precipitation, air quality index, earthquake belt and fixed assets of information transmission enterprises, the mathematical model of evaluating the operation advantage of green data centre is constructed, to made quantitative analysis and evaluation classification, and according to the calculation, the comparison results of green data centre development area are obtained.

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Section: Research On Key Indicators and Regional Comparison Of Green ...mentioning

confidence: 99%

Research on key indicators and regional comparison of green data centre

Mei

Feng

Wen

2021

IJRIS

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“…In terms of PSD estimation, especially for noise and interfering sources, methods based on minimum statistics [19,20], minimum mean squared error (MMSE) [21][22][23], and minima controlled recursive averaging (MCRA) [24][25][26][27][28] have set the baseline for allowing noise reduction and speech enhancement methods to be developed. Later, subspace-based methods [29][30][31], as well as deep learning methods [32][33][34][35], provided different perspectives on how to perform PSD estimation.…”

Section: Introductionmentioning

confidence: 99%

Direction-of-arrival and power spectral density estimation using a single directional microphone and group-sparse optimization

Tengan,

Dietzen,

Elvander

et al. 2023

J AUDIO SPEECH MUSIC PROC.

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In this paper, two approaches are proposed for estimating the direction of arrival (DOA) and power spectral density (PSD) of stationary point sources by using a single, rotating, directional microphone. These approaches are based on a method previously presented by the authors, in which point source DOAs were estimated by using a broadband signal model and solving a group-sparse optimization problem, where the number of observations made by the rotating directional microphone can be lower than the number of candidate DOAs in an angular grid. The DOA estimation is followed by the estimation of the sources’ PSDs through the solution of an overdetermined least squares problem. The first approach proposed in this paper includes the use of an additional nonnegativity constraint on the residual noise term when solving the group-sparse optimization problem and is referred to as the Group Lasso Least Squares (GL-LS) approach. The second proposed approach, in addition to the new nonnegativity constraint, employs a narrowband signal model when building the linear system of equations used for formulating the group-sparse optimization problem, where the DOAs and PSDs can be jointly estimated by iterative, group-wise reweighting. This is referred to as the Group-Lasso with $$l_1$$ l 1 -reweighting (GL-L1) approach. Both proposed approaches are implemented using the alternating direction method of multipliers (ADMM), and their performance is evaluated through simulations in which different setup conditions are considered, ranging from different types of model mismatch to variations in the acoustic scene and microphone directivity pattern. The results obtained show that in a scenario involving a microphone response mismatch between observed data and the signal model used, having the additional nonnegativity constraint on the residual noise can improve the DOA estimation for the case of GL-LS and the PSD estimation for the case of GL-L1. Moreover, the GL-L1 approach can present an advantage over GL-LS in terms of DOA estimation performance in scenarios with low SNR or where multiple sources are closely located to each other. Finally, it is shown that having the least squares PSD re-estimation step is beneficial in most scenarios, such that GL-LS outperformed GL-L1 in terms of PSD estimation errors.

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Speech enhancement using group complementary joint sparse representations in modulation domain

Xie

Yang

2022

Applied Acoustics

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On single-channel noise reduction with rank-deficient noise correlation matrix

Cited by 7 publications

References 18 publications

Research on key indicators and regional comparison of green data centre

Research on key indicators and regional comparison of green data centre

Direction-of-arrival and power spectral density estimation using a single directional microphone and group-sparse optimization

Speech enhancement using group complementary joint sparse representations in modulation domain

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