Results 11 to 20 of about 142 (104)
[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
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
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
doaj +2 more sources
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
doaj +1 more source
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
Dereverberation by Using Time-Variant Nature of Speech Production System
EURASIP Journal on Advances in Signal Processing, 2007 This paper addresses the problem of blind speech dereverberation by inverse filtering of a room acoustic system. Since a speech signal can be modeled as being generated by a speech production system driven by an innovations process, a reverberant signal ...
Masato Miyoshi +2 more
doaj +2 more sources
EURASIP Journal on Advances in Signal Processing, 2017 A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system.
Bo Wu +6 more
doaj +1 more source
EURASIP Journal on Audio, Speech, and Music Processing, 2023 More and more smart home devices with microphones come into our life in these years; it is highly desirable to connect these microphones as wireless acoustic sensor networks (WASNs) so that these devices can be better controlled in an enclosure.
Zhe Han +3 more
doaj +1 more source
Single-Channel Speech Enhancement Techniques for Distant Speech Recognition
Journal of Intelligent Systems, 2013 This article presents an overview of the single-channel dereverberation methods suitable for distant speech recognition (DSR) application. The dereverberation methods are mainly classified based on the domain of enhancement of speech signal captured by a
Ashwini Jaya Kumar +1 more
doaj +1 more source