Results 11 to 20 of about 926 (186)
Deep Learning-Based Amplitude Fusion for Speech Dereverberation
Mapping and masking are two important speech enhancement methods based on deep learning that aim to recover the original clean speech from corrupted speech. In practice, too large recovery errors severely restrict the improvement in speech quality.
Chunlei Liu, Longbiao Wang, Jianwu Dang
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Speech Dereverberation with a Reverberation Time Shortening Target
This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training.
Rui Zhou, Wenye Zhu, Xiaofei Li
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The effect of reverberation on speech is to cause it to sound distant and spectrally distorted and can also reduce intelligibility. Dereverberation is therefore an important speech enhancement process for hands-free terminals. This is a blind problem and currently an unsolved problem. This paper reviews existing ap- proaches and discuss current work on
Naylor, Patrick, Gaubitch, Nikolay
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A Hybrid Model for Weakly-Supervised Speech Dereverberation
This paper introduces a new training strategy to improve speech dereverberation systems using minimal acoustic information and reverberant (wet) speech. Most existing algorithms rely on paired dry/wet data, which is difficult to obtain, or on target metrics that may not adequately capture reverberation characteristics and can lead to poor results on ...
Bahrman, Louis +2 more
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pykanto: A python library to accelerate research on wild bird song
Abstract Studying the vocalisations of wild animals can be a challenge due to the limitations of traditional computational methods, which often are time‐consuming and lack reproducibility. Here, I present pykanto, a new software package that provides a set of tools to build, manage, and explore large sound databases.
Nilo Merino Recalde
wiley +1 more source
Abstract Various time‐frequency (T‐F) masks are being applied to sound source localization tasks. Moreover, deep learning has dramatically advanced T‐F mask estimation. However, existing masks are usually designed for speech separation tasks and are suitable only for single‐channel signals.
Hong Liu +4 more
wiley +1 more source
[Retracted] Serialized Recommendation Technology Based on Deep Neural Network
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
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
Channel and temporal-frequency attention UNet for monaural speech enhancement
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
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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