An Iterative Graph Spectral Subtraction Method for Speech Enhancement

Yan, Xue; Yang, Zhen; Wang, Tingting; Guo, Haiyan

doi:10.1016/j.specom.2020.06.005

Cited by 27 publications

(11 citation statements)

References 23 publications

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“…• Results and paper must be submitted within the deadlines shown in Subsection 5.4 via the IEEE MLSP 2021 submission site 10 .…”

Section: Paper and Results Submissionsmentioning

confidence: 99%

“…Also U-Net-based approaches provide competitive results in this context, both for monaural [6,7] and multichannel SE tasks [8], at the expense of higher computational power demand. Other techniques to perform SE include recurrent neural networks (RNNs) [9], graph-based spectral subtraction [10], discriminative learning [11], dilated convolutions [12,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

Guizzo¹,

Gramaccioni²,

Jamili³

et al. 2021

Preprint

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The L3DAS21 Challenge 1 is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dual-mic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and SELDnet for SELD.This report is aimed at providing all needed information to participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21 dataset, the challenge tasks and the baseline models.

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“…• Results and paper must be submitted within the deadlines shown in Subsection 5.4 via the IEEE MLSP 2021 submission site 10 .…”

Section: Paper and Results Submissionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

Guizzo¹,

Gramaccioni²,

Jamili³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Among these concepts, Graph filter and GFT are important tools to process the graph signal. They have shown their advantage in graph neural networks [7,8], graph signal denoising [9,10,11], graph signal recovery [12], speech enhancement [13,14] and others.…”

Section: Introductionmentioning

confidence: 99%

Optimal Fractional Fourier Filtering in Time-vertex Graphs signal processing

Ge¹,

Guo²,

Wang³

et al. 2022

Preprint

Self Cite

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Graph signal processing (GSP) is an effective tool in dealing with data residing on irregular domains. In GSP, the optimal graph filter is one of the essential techniques, owing to its ability to recover the original signal from the distorted and noisy version. However, most current research focuses on static graph signals and ordinary space/time or frequency domains. The time-varying graph signals have a strong ability to capture the features of real-world data, and fractional domains can provide a more suitable space to separate the signal and noise. In this paper, the optimal time-vertex graph filter and its Wiener-Hopf equation are developed, using the product graph framework. Furthermore, the optimal time-vertex graph filter in fractional domains is also developed, using the graph fractional Laplacian operator and graph fractional Fourier transform.Numerical simulations on real-world datasets will demonstrate the superiority of the optimal time-vertex graph filter in fractional domains over the optimal time-vertex graph filter in ordinary domains and the optimal static graph filter in fractional domains.

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“…It aims to reserve the desired graph frequencies and attenuate the others. Due to its wide range of applications, graph filter design has attracted increasing attention and shown its advantage in graph wavelets [3,4], graph signal denoising [5][6][7], graph signal recovery [8], speech enhancement [9,10] and others.…”

Section: Introductionmentioning

confidence: 99%

Universal Graph Filter Design based on Butterworth, Chebyshev and Elliptic Functions

Ge¹,

Guo²,

Wang³

et al. 2022

Preprint

Self Cite

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Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and Elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high pass graph filter, Chebyshev high pass graph filter, and Elliptic high pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average (ARMA) graph filter design method, the linear least-squares fitting (LLS) based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.

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An Iterative Graph Spectral Subtraction Method for Speech Enhancement

Cited by 27 publications

References 23 publications

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

Optimal Fractional Fourier Filtering in Time-vertex Graphs signal processing

Universal Graph Filter Design based on Butterworth, Chebyshev and Elliptic Functions

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