Results 11 to 20 of about 2,562 (160)
Deep Learning Based Target Cancellation for Speech Dereverberation. [PDF]
IEEE/ACM Trans Audio Speech Lang Process, 2020 This study investigates deep learning based single- and multi-channel speech dereverberation. For single-channel processing, we extend magnitude-domain masking and mapping based dereverberation to complex-domain mapping, where deep neural networks (DNNs) are trained to predict the real and imaginary (RI) components of the direct-path signal from ...
Wang ZQ, Wang D.
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Multimicrophone Speech Dereverberation: Experimental Validation
EURASIP Journal on Audio, Speech, and Music Processing, 2007 Dereverberation is required in various speech processing applications such as handsfree telephony and voice-controlled systems, especially when signals are applied that are recorded in a moderately or highly reverberant environment.
Moonen Marc, Eneman Koen
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Subspace Methods for Multimicrophone Speech Dereverberation [PDF]
EURASIP Journal on Advances in Signal Processing, 2003 A novel approach for multimicrophone speech dereverberation is presented. The method is based on the construction of the null subspace of the data matrix in the presence of colored noise, using the generalized singular-value decomposition (GSVD ...
Moonen Marc, Gannot Sharon
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Monaural Speech Dereverberation Using Temporal Convolutional Networks with Self Attention. [PDF]
IEEE/ACM Trans Audio Speech Lang Process, 2020 Zhao Y, Wang D, Xu B, Zhang T.
europepmc +2 more sources
Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising. [PDF]
IEEE/ACM Trans Audio Speech Lang Process, 2017 Williamson DS, Wang D.
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CycleGAN-based Unpaired Speech Dereverberation
Interspeech 2022, 2022 Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance. The main limitation of this approach is that such models can only be trained on large amounts of data and a variety of room impulse responses when the data is synthetically reverberated ...
Muckenhirn, Hannah +6 more
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Machine Learning for Predictive Analytics in the Improvement of English Speech Feature Recognition
Mobile Information Systems, Volume 2022, Issue 1, 2022., 2022 The use of deep learning to improve English speaking has seen tremendous development in recent years. This study evaluates the noise that is present in the English speech environment, employs a two‐way search method to select the optimum feature set, and applies a quick correlation filter to remove redundant features in order to increase the accuracy ...
Yan Chen +2 more
wiley +1 more source
Scene-Agnostic Multi-Microphone Speech Dereverberation [PDF]
Interspeech 2021, 2021 Neural networks (NNs) have been widely applied in speech processing tasks, and, in particular, those employing microphone arrays. Nevertheless, most existing NN architectures can only deal with fixed and position-specific microphone arrays. In this paper, we present an NN architecture that can cope with microphone arrays whose number and positions of ...
Yemini, Yochai +3 more
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Channel and temporal-frequency attention UNet for monaural speech enhancement
EURASIP Journal on Audio, Speech, and Music Processing, 2023 The presence of noise and reverberation significantly impedes speech clarity and intelligibility. To mitigate these effects, numerous deep learning-based network models have been proposed for speech enhancement tasks aimed at improving speech quality. In
Shiyun Xu, Zehua Zhang, Mingjiang Wang
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Effective Dereverberation with a Lower Complexity at Presence of the Noise
Applied Sciences, 2022 Adaptive beamforming and deconvolution techniques have shown effectiveness for reducing noise and reverberation. The minimum variance distortionless response (MVDR) beamformer is the most widely used for adaptive beamforming, whereas multichannel linear ...
Fengqi Tan, Changchun Bao, Jing Zhou
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