IDANet: An Information Distillation and Aggregation Network for Speech Enhancement

Tai, Wenxin; Lan, Tian; Wang, Qianhui; Liu, Qiao

doi:10.1109/lsp.2021.3114122

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…However, they often fail when faced with non-stationary scenarios. Recently, this issue has been relatively well alleviated with approaches based on the deep learning (DL) paradigms (Zhao et al 2016;Tai et al 2021a).…”

Section: Introductionmentioning

confidence: 99%

Revisiting Denoising Diffusion Probabilistic Models for Speech Enhancement: Condition Collapse, Efficiency and Refinement

Tai

Zhang

Trajcevski

et al. 2023

AAAI

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Recent literature has shown that denoising diffusion probabilistic models (DDPMs) can be used to synthesize high-fidelity samples with a competitive (or sometimes better) quality than previous state-of-the-art approaches. However, few attempts have been made to apply DDPM for the speech enhancement task. The reported performance of the existing works is relatively poor and significantly inferior to other generative methods. In this work, we first reveal the difficulties in applying existing diffusion models to the field of speech enhancement. Then we introduce DR-DiffuSE, a simple and effective framework for speech enhancement using conditional diffusion models. We present three strategies (two in diffusion training and one in reverse sampling) to tackle the condition collapse and guarantee the sufficient use of condition information. For efficiency, we introduce the fast sampling technique to reduce the sampling process into several steps and exploit a refinement network to calibrate the defective speech. Our proposed method achieves the state-of-the-art performance to the GAN-based model and shows a significant improvement over existing DDPM-based algorithms.

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Section: Introductionmentioning

confidence: 99%

Revisiting Denoising Diffusion Probabilistic Models for Speech Enhancement: Condition Collapse, Efficiency and Refinement

Tai

Zhang

Trajcevski

et al. 2023

AAAI

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show abstract

“…Recently, the progress of deep learning algorithms has brought substantial improvements also in the SE field [ 14 , 15 , 16 , 17 , 18 , 19 ]. Deep learning techniques are data-driven approaches that frames the SE task as a supervised learning problem aiming at reconstructing the target speech signals from the noisy mixture.…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Joint Training Strategies of Speech Enhancement and Intent Classification Neural Models

Ali

Falavigna

Brutti

2022

Sensors

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Robustness against background noise and reverberation is essential for many real-world speech-based applications. One way to achieve this robustness is to employ a speech enhancement front-end that, independently of the back-end, removes the environmental perturbations from the target speech signal. However, although the enhancement front-end typically increases the speech quality from an intelligibility perspective, it tends to introduce distortions which deteriorate the performance of subsequent processing modules. In this paper, we investigate strategies for jointly training neural models for both speech enhancement and the back-end, which optimize a combined loss function. In this way, the enhancement front-end is guided by the back-end to provide more effective enhancement. Differently from typical state-of-the-art approaches employing on spectral features or neural embeddings, we operate in the time domain, processing raw waveforms in both components. As application scenario we consider intent classification in noisy environments. In particular, the front-end speech enhancement module is based on Wave-U-Net while the intent classifier is implemented as a temporal convolutional network. Exhaustive experiments are reported on versions of the Fluent Speech Commands corpus contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, shedding light and providing insight about the most promising training approaches.

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GAN-in-GAN for Monaural Speech Enhancement

Duan

Ren

et al. 2023

IEEE Signal Process. Lett.

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IDANet: An Information Distillation and Aggregation Network for Speech Enhancement

Cited by 3 publications

References 31 publications

Revisiting Denoising Diffusion Probabilistic Models for Speech Enhancement: Condition Collapse, Efficiency and Refinement

Revisiting Denoising Diffusion Probabilistic Models for Speech Enhancement: Condition Collapse, Efficiency and Refinement

Time-Domain Joint Training Strategies of Speech Enhancement and Intent Classification Neural Models

GAN-in-GAN for Monaural Speech Enhancement

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