A study on target feature activation and normalization and their impacts on the performance of DNN based speech dereverberation systems

Li, Kehuang; Yang, Minglei; Lee, Chin‐Hui

doi:10.1109/apsipa.2016.7820875

Cited by 12 publications

(13 citation statements)

References 16 publications

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“…In [11], the authors performed a study on target feature activation and normalization and their impacts on the performance of DNN based speech dereverberation systems. The authors compared the target activation/normalization scheme in [8] with a linear (unbounded) activation function and output normalized by its mean and variance.…”

Section: Introductionmentioning

confidence: 99%

Speech Dereverberation With Context-Aware Recurrent Neural Networks

Santos

Falk

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this paper, we propose a model to perform speech dereverberation by estimating its spectral magnitude from the reverberant counterpart. Our models are capable of extracting features that take into account both short and long-term dependencies in the signal through a convolutional encoder (which extracts features from a short, bounded context of frames) and a recurrent neural network for extracting long-term information. Our model outperforms a recently proposed model that uses different context information depending on the reverberation time, without requiring any sort of additional input, yielding improvements of up to 0.4 on PESQ, 0.3 on STOI, and 1.0 on POLQA relative to reverberant speech. We also show our model is able to generalize to real room impulse responses even when only trained with simulated room impulse responses, different speakers, and high reverberation times. Lastly, listening tests show the proposed method outperforming benchmark models in reduction of perceived reverberation.

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Section: Introductionmentioning

confidence: 99%

Speech Dereverberation With Context-Aware Recurrent Neural Networks

Santos

Falk

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…In this paper, we propose a third class of single-channel late reverberation PSD estimators based on denoising autoencoders (DAs) [12,13]. In the context of dereverberation, DAs have already been used for generating robust dereverberated features for speech recognition [14,15] as well as for enhancing reverberant speech [16][17][18]. In [16][17][18], a DA has been used to learn a spectral mapping from the magnitude spectrogram of reverberant speech to the magnitude spectrogram of clean speech.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of dereverberation, DAs have already been used for generating robust dereverberated features for speech recognition [14,15] as well as for enhancing reverberant speech [16][17][18]. In [16][17][18], a DA has been used to learn a spectral mapping from the magnitude spectrogram of reverberant speech to the magnitude spectrogram of clean speech. In [18] it is shown that by incorporating information of the reverberation time during the training stage, the dereverberation performance can be further improved.…”

Section: Introductionmentioning

confidence: 99%

“…In [18] it is shown that by incorporating information of the reverberation time during the training stage, the dereverberation performance can be further improved. In the present approach, instead of estimating the clean speech magnitude spectrogram from the reverberant speech magnitude spectrogram as in [16][17][18], we propose to use a DA to estimate the late reverberation PSD from the microphone signal PSD. The estimated late reverberation PSD can then be used in a spectral enhancement technique such as the Wiener filter in order to achieve dereverberation.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, such an approach gives the user the flexibility to select the most advantageous spectral enhancement technique to use depending on the application. Our proposed approach differs from [16][17][18] not only in estimating the late reverberation PSD instead of the clean speech magnitude spectrogram, but also in the used DA architecture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Single-channel Late Reverberation Power Spectral Density Estimation Using Denoising Autoencoders

Kodrasi

Bourlard

2018

Interspeech 2018

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In order to suppress the late reverberation in the spectral domain, many single-channel dereverberation techniques rely on an estimate of the late reverberation power spectral density (PSD). In this paper, we propose a novel approach to late reverberation PSD estimation using a denoising autoencoder (DA), which is trained to learn a mapping from the microphone signal PSD to the late reverberation PSD. Simulation results show that the proposed approach yields a high PSD estimation accuracy and generalizes well to unseen data. Furthermore, simulation results show that the proposed DA-based PSD estimate yields a higher PSD estimation accuracy and a similar dereverberation performance than a state-of-the-art statistical PSD estimate, which additionally also requires knowledge of the reverberation time.

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A study on sampling of STFT modifications in time and frequency domains for DNN-based speech dereverberation

Yang

Lee

2016

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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A study on target feature activation and normalization and their impacts on the performance of DNN based speech dereverberation systems

Cited by 12 publications

References 16 publications

Speech Dereverberation With Context-Aware Recurrent Neural Networks

Speech Dereverberation With Context-Aware Recurrent Neural Networks

Single-channel Late Reverberation Power Spectral Density Estimation Using Denoising Autoencoders

A study on sampling of STFT modifications in time and frequency domains for DNN-based speech dereverberation

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