Results 11 to 20 of about 1,864,117 (195)
Noise Perturbation for Supervised Speech Separation [PDF]
Speech Communication, 2016 Speech separation can be treated as a mask estimation problem, where interference-dominant portions are masked in a time-frequency representation of noisy speech. In supervised speech separation, a classifier is typically trained on a mixture set of speech and noise.
Jitong Chen, DeLiang Wang, Yuxuan Wang
exaly +6 more sources
Speech separation by kurtosis maximization
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002 We present a computationally efficient method of separating mixed speech signals. The method uses a recursive adaptive gradient descent technique with the cost function designed to maximize the kurtosis of the output (separated) signals. The choice of kurtosis maximization as an objective function (which acts as a measure of separation) is supported by
James P. LeBlanc, Phillip L. De Leon
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Rethinking The Separation Layers In Speech Separation Networks [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Modules in all existing speech separation networks can be categorized into single-input-multi-output (SIMO) modules and single-input-single-output (SISO) modules. SIMO modules generate more outputs than input, and SISO modules keep the numbers of input and output the same.
Yi Luo 0004 +5 more
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Target Speaker Extraction by Fusing Voiceprint Features
Applied Sciences, 2022 It is a critical problem to accurately separate clean speech in the multispeaker scenario for different speakers. However, in most cases, smart devices such as smart phones interact with only one specific user.
Shidan Cheng, Ying Shen, Dongqing Wang
doaj +1 more source
Language Identification-Based Evaluation of Single Channel Speech Separation of Overlapped Speeches
Information, 2022 In multi-lingual, multi-speaker environments (e.g., international conference scenarios), speech, language, and background sounds can overlap. In real-world scenarios, source separation techniques are needed to separate target sounds.
Zuhragvl Aysa +3 more
doaj +1 more source
Reverberation as Supervision For Speech Separation
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 5 pages, 2 figures, 4 tables.
Rohith Aralikatti +4 more
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Applied Sciences, 2022 Speech separation is a hot topic in multi-speaker speech recognition. The long-term autocorrelation of speech signal sequences is an essential task for speech separation.
Yanji Jiang +4 more
doaj +1 more source
On Training Targets for Supervised Speech Separation. [PDF]
IEEE/ACM Trans Audio Speech Lang Process, 2014 Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest.
Wang Y, Narayanan A, Wang D.
europepmc +4 more sources
Data Augmentation for Speech Separation
Speech Communication, 2022 Deep learning models have advanced the state of the art of monaural speech separation. However, the performance of the separation model considerably decreases when tested on unseen speakers and unseen noisy conditions. Separation models trained with data augmentation generalize better to unseen conditions.
Alex A. +3 more
openaire +2 more sources
Sparse separation of sources in 3D soundscapes [PDF]
, 2010 A novel blind source separation algorithm applicable to extracting sources from within 3D soundscapes is presented. The algorithm is based on constructing a binary mask based on directional information. The validity of filtering using binary masked based
Bunting, Oliver
core +4 more sources