FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective functions

Zhu, Yuanyuan; Xu, Xu; Ye, Zhongfu

doi:10.1016/j.apacoust.2020.107511

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…whereS cm r = |S cm |e jθ X andS cm i = |S cm |e jθ X . Both CME-Net and CSR-Net take the similar network topology as [13], which includes the gated convolutional encoder, decoder and stacked temporal convolution modules (dubbed TCMs) [14]. The encoder is utilized to extract the spectral patterns of the spectrum while the decoder is to reconstruct the spectrum.…”

Section: Two-stage Networkmentioning

confidence: 99%

ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network

Liu

Luo

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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It remains a tough challenge to recover the speech signals contaminated by various noises under real acoustic environments. To this end, we propose a novel system for denoising in the complicated applications, which is mainly comprised of two pipelines, namely a two-stage network and a post-processing module. The first pipeline is proposed to decouple the optimization problem w.r.t. magnitude and phase, i.e., only the magnitude is estimated in the first stage and both of them are further refined in the second stage. The second pipeline aims to further suppress the remaining unnatural distorted noise, which is demonstrated to sufficiently improve the subjective quality. In the ICASSP 2021 Deep Noise Suppression (DNS) Challenge, our submitted system ranked top-1 for the real-time track 1 in terms of Mean Opinion Score (MOS) with ITU-T P.808 framework.

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Section: Two-stage Networkmentioning

confidence: 99%

ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network

Liu

Luo

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Compared to the existing models, the proposed method achieves higher SDR, PESQ, CORR, ESTOI, STOI, and SNR, as well as lower RMSE values. Moreover, the proposed model overcomes the drawbacks such as reduction in speech intelligibility [ 43 ], lower PESQ [ 40 ], lower robustness [ 37 ], not being suitable for complex noise environments [ 37 ], lower speech quality [ 10 ], and low SNR [ 45 ] [ 29 ]. However, this proposed method lacks at some noise sources and it did not determine the spectral magnitude and spectral phase estimation.…”

Section: Resultsmentioning

confidence: 99%

“…In 2020, Tan et al [ 45 ] introduced a Fully Convolutional Neural Network (FCNN) “to achieve end-to-end speech enhancement. The encoder and decoder, as well as an extra Convolutional-Based Short-Time Fourier Transform (CSTFT) layer and CISTFT layer, were applied to simulate forward as well as inverse STFT operations, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most studies have shown that reducing signal noise without distorting speech is a difficult challenge, which is one of the main reasons why perfect enhancement systems aren’t available. In this research, we focus the issues such as lower robustness, not suitable for complex noise conditions [ 37 ], more residual noise existing, lower SNR [ 45 ], reduction in speech intelligibility [ 43 ], lower denoise effect, lower PESQ [ 40 ], and low consideration of speech quality measures [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Speech enhancement using long short term memory with trained speech features and adaptive wiener filter

Garg

2022

Multimed Tools Appl

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Speech enhancement is the process of enhancing the clarity and intelligibility of speech signals that have been degraded due to background noise. With the assistance of deep learning, a novel speech signal enhancement model is introduced in this research. The proposed model is divided into two phases: (i) Training (ii) Testing. In the training phase, the noise spectrum and signal spectrum are estimated via a Non-negative Matrix Factorization (NMF) from the noisy input signal. Then, Empirical Mean Decomposition (EMD) features are extracted from the Wiener filter. The de-noised signal is acquired from EMD, the bark frequency is evaluated and the Fractional Delta AMS features are extracted. The key contribution of this study is the use of the Long Short Term Memory (LSTM) model to properly estimate the tuning factor η of the Wiener filter for all input signals. The LSTM was trained by the extracted features (EMD) via a modified wiener filter for decomposing the spectral signal and the output of EMD is the denoised enhanced speech signal. A comparative evaluation is carried out between the proposed and existing models in terms of error measures.

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“…Both CME-Net and CSR-Net take the similar network topology as [13], which includes the gated convolutional encoder, decoder and stacked temporal convolution modules (dubbed TCMs) [14]. The encoder is utilized to extract the spectral patterns of the spectrum while the decoder is to reconstruct the spectrum.…”

Section: Two-stage Networkmentioning

confidence: 99%

ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network

Liu

Luo

et al. 2021

Preprint

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show abstract

FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective functions

Cited by 18 publications

References 20 publications

ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network

ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network

Speech enhancement using long short term memory with trained speech features and adaptive wiener filter

ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network

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