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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Proceedings ArticleDOI
Comparison of CNN-based Speech Dereverberation using Neural Vocoder
TL;DR: In this article, the performance of the CNN-based dereverberation method by applying various vocoders was compared with the reverberation removal and vocoder using the U-Net architecture.
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TL;DR: Alternative online weighted least squares algorithms are derived through Householder RLS and Householder least squares lattice (HLSL), which are numerically stable and retain the fast convergence capability of the RLS algorithm.
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