Neural Network-Based Spectrum Estimation for Online WPE Dereverberation

Kinoshita, Keisuke; Delcroix, Marc; Kwon, Haeyong; Mori, Takuro; Nakatani, Tomohiro

doi:10.21437/interspeech.2017-733

Cited by 88 publications

(99 citation statements)

References 24 publications

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“…We compare the performance with the state-of-the-art dereverberation method called Weighted Prediction Error (WPE), which is known to effectively reduce reverberation and greatly boosts the speech enhancement performance. We used the more recent version of WPE [29] which is also based on DNN [30]. However, WPE uses a different architecture based on LSTM.…”

Section: Reference and Performance Assessmentmentioning

confidence: 99%

Progressive Speech Enhancement with Residual Connections

et al. 2019

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This paper studies the Speech Enhancement based on Deep Neural Networks. The proposed architecture gradually follows the signal transformation during enhancement by means of a visualization probe at each network block. Alongside the process, the enhancement performance is visually inspected and evaluated in terms of regression cost. This progressive scheme is based on Residual Networks. During the process, we investigate a residual connection with a constant number of channels, including internal state between blocks, and adding progressive supervision. The insights provided by the interpretation of the network enhancement process leads us to design an improved architecture for the enhancement purpose. Following this strategy, we are able to obtain speech enhancement results beyond the state-of-the-art, achieving a favorable trade-off between dereverberation and the amount of spectral distortion.

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Section: Reference and Performance Assessmentmentioning

confidence: 99%

Progressive Speech Enhancement with Residual Connections

et al. 2019

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“…4 in [110] describe the NN, which operates in the log-spectral domain, that is used for pre-cleaning the noisy and reverberant speech signal. The pre-cleaned power spectral domain is then used for the WPE algorithm; a particular feature of the algorithm in [110] is that the WPE method does not need more than one iterations.…”

Section: Additional Literature Reviewmentioning

confidence: 99%

Phase-Aware Single-Channel Speech Enhancement With Modulation-Domain Kalman Filtering

Dionelis

Brookes

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-We present a speech enhancement algorithm that performs modulation-domain Kalman filtering to track the speech phase using circular statistics, along with the log-spectra of speech and noise. In the proposed algorithm, the speech phase posterior is used to create an enhanced speech phase spectrum for the signal reconstruction of speech. The Kalman filter prediction step separately models the temporal inter-frame correlation of the speech and noise spectral log-amplitudes and of the speech phase, while the Kalman filter update step models their nonlinear relations under the assumption that speech and noise add in the complex short-time Fourier transform domain. The phasesensitive enhancement algorithm is evaluated with speech quality and intelligibility metrics, using a variety of noise types over a range of SNRs. Instrumental measures predict that tracking the speech log-spectrum and phase with modulation-domain Kalman filtering leads to consistent improvements in speech quality, over both conventional enhancement algorithms and other algorithms that perform modulation-domain Kalman filtering.

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“…The latter showing improvements upon RNN based methods [55] in terms of PESQ and STOI scores. For noise supression and dereverberation, authors in [56] proposed to use an RNN to estimate the power spectral density (PSD) prior to prediction filter estimation and inverse filtering, showing ASR improvements compared to the baseline method that iteratively calculated PSD on the enhanced speech signal. None of these techniques have been evaluated in terms of how well they can improve TTS quality.…”

Section: Our Work In Contextmentioning

confidence: 99%

Speech Enhancement of Noisy and Reverberant Speech for Text-to-Speech

Valentini-Botinhao

Yamagishi

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Text-to-speech voices created from noisy and reverberant recordings are of lower quality. A simple way to improve this is to increase the quality of the recordings prior to textto-speech training with speech enhancement methods such as noise suppression and dereverberation. In this paper we opted for this approach and to perform the enhancement we used a recurrent neural network. The network is trained with parallel data of clean and lower quality recordings of speech. The lower quality data was artificially created by adding recordings of environmental noise to studio quality recordings of speech and by convolving room impulse responses with these clean recordings. We trained separate networks with noise only, reverberation only and both reverberation and additive noise data. The quality of voices trained with lower quality data that has been enhanced using these networks was significantly higher in all cases. For the noise only case, the enhanced synthetic voice ranked as high as the voice trained with clean data. For the most realistic and challenging scenario, when both noise and reverberation were present, the improvements were more modest, but still significant.

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Neural Network-Based Spectrum Estimation for Online WPE Dereverberation

Cited by 88 publications

References 24 publications

Progressive Speech Enhancement with Residual Connections

Progressive Speech Enhancement with Residual Connections

Phase-Aware Single-Channel Speech Enhancement With Modulation-Domain Kalman Filtering

Speech Enhancement of Noisy and Reverberant Speech for Text-to-Speech

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