Results 21 to 30 of about 2,995 (171)
A Multi-Source Separation Approach Based on DOA Cue and DNN
Applied Sciences, 2022 Multiple sound source separation in a reverberant environment has become popular in recent years. To improve the quality of the separated signal in a reverberant environment, a separation method based on a DOA cue and a deep neural network (DNN) is ...
Yu Zhang +3 more
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[Retracted] Serialized Recommendation Technology Based on Deep Neural Network
Wireless Communications and Mobile Computing, Volume 2022, Issue 1, 2022., 2022 Since the construction of brain network is like organic brain organization, profound brain network has high effectiveness and high accuracy in separating data from profound elements, fit for multifacet learning, conceptual component portrayal, cross‐space learning capacity, multisource, heterogeneous data content.
Long Jin, Chia-Huei Wu
wiley +1 more source
Applied Sciences, 2021 The estimation of the late reverberant spectral variance (LRSV) is of paramount importance in most reverberation suppression algorithms. This letter proposes an improved single-channel LRSV estimator based on Habets LRSV estimator by using an adaptive ...
Zhaoqi Zhang, Xuelei Feng, Yong Shen
doaj +1 more source
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
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
openaire +2 more sources
Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
Applied Sciences, 2023 Weighted prediction error (WPE) is a linear prediction-based method extensively used to predict and attenuate the late reverberation component of an observed speech signal.
Tomer Rosenbaum +2 more
doaj +1 more source
Learning Audio-Visual Dereverberation
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Reverberation not only degrades the quality of speech for human perception, but also severely impacts the accuracy of automatic speech recognition. Prior work attempts to remove reverberation based on the audio modality only. Our idea is to learn to dereverberate speech from audio-visual observations.
Chen, Changan +3 more
openaire +2 more sources
Spherical microphone array acoustic rake receivers [PDF]
, 2015 Several signal independent acoustic rake receivers are proposed for speech dereverberation using spherical microphone arrays. The proposed rake designs take advantage of multipaths, by separately capturing and combining early reflections with the direct ...
Javed, HA, Moore, AH, Naylor, PA
core +1 more source
Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function [PDF]
, 2018 This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, \emph{assuming known mixing filters}.
Gannot, Sharon +3 more
core +3 more sources
A multichannel learning-based approach for sound source separation in reverberant environments
EURASIP Journal on Audio, Speech, and Music Processing, 2021 In this paper, a multichannel learning-based network is proposed for sound source separation in reverberant field. The network can be divided into two parts according to the training strategies.
You-Siang Chen +2 more
doaj +1 more source