Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising
TLDR
This paper performs dereverberation and denoising using supervised learning with a deep neural network and defines the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech.Abstract:
In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.read more
Citations
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Subband Kalman Filtering with DNN Estimated Parameters for Speech Enhancement.
TL;DR: Experimental results show that the proposed system outperforms three Kalman filtering based methods in terms of both speech quality and intelligibility.
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Deep ad-hoc beamforming based on speaker extraction for target-dependent speech separation
TL;DR: In this paper , the authors proposed a target-dependent beamforming based on speaker extraction, which is to the best of our knowledge the first work for targetdependent speech separation based on ad-hoc microphone arrays and deep learning.
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Monaural Source Separation Based on Sequentially Trained LSTMs in Real Room Environments
Yi Li,Yang Sun,Syed Mohsen Naqvi +2 more
TL;DR: Dereverberation mask (DM) is introduced and a system to train two SA-LSTMs sequentially, which dereverberate speech mixture and improve the performance is established, which outperforms the state-of-the-art.
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Speech enhancement using U-nets with wide-context units
Tomasz Grzywalski,Szymon Drgas +1 more
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A hybrid speech enhancement system with DNN based speech reconstruction and Kalman filtering
TL;DR: Compared to the DNN based methods, the hybrid system achieves similar performance under seen noise, but notably better performance under unseen noise, in terms of both speech quality and intelligibility.
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