Deep-Learning Framework for Efficient Real-Time Speech Enhancement and Dereverberation [PDF]
SensorsDeep learning has revolutionized speech enhancement, enabling impressive high-quality noise reduction and dereverberation. However, state-of-the-art methods often demand substantial computational resources, hindering their deployment on edge devices and ...
Israel Cohen
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Deep Learning-Based Estimation of Reverberant Environment for Audio Data Augmentation [PDF]
Sensors, 2022 This paper proposes an audio data augmentation method based on deep learning in order to improve the performance of dereverberation. Conventionally, audio data are augmented using a room impulse response, which is artificially generated by some methods ...
Deokgyu Yun, Seung Ho Choi
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Joint Optimization of Deep Neural Network-Based Dereverberation and Beamforming for Sound Event Detection in Multi-Channel Environments [PDF]
Sensors, 2020 In this paper, we propose joint optimization of deep neural network (DNN)-supported dereverberation and beamforming for the convolutional recurrent neural network (CRNN)-based sound event detection (SED) in multi-channel environments.
Kyoungjin Noh, Joon-Hyuk Chang
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Cortical adaptation to sound reverberation [PDF]
eLife, 2022 In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation.
Aleksandar Z Ivanov +4 more
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A Robust Bilinear Framework for Real-Time Speech Separation and Dereverberation in Wearable Augmented Reality [PDF]
SensorsThis paper presents a bilinear framework for real-time speech source separation and dereverberation tailored to wearable augmented reality devices operating in dynamic acoustic environments.
Alon Nemirovsky +2 more
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Deep Learning-Based Amplitude Fusion for Speech Dereverberation
Discrete Dynamics in Nature and Society, 2020 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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A dereverberation beamforming algorithm for noise source localization in anechoic and semi-reverberant environments [PDF]
MethodsXThis paper presents a dereverberation beamforming (DBF) technique based on windowing the cross-correlation matrix (CCM) to improve the localization accuracy of beamforming maps for imaging the noise sources generated by real-world applications. Following
R. Singh, A. Mimani, R. Kumar
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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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Triple-0: Zero-shot denoising and dereverberation on an end-to-end frozen anechoic speech separation network. [PDF]
PLoS ONESpeech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning ...
Sania Gul +2 more
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Signal-Based Performance Evaluation of Dereverberation Algorithms
Journal of Electrical and Computer Engineering, 2010 We address the measurement of reverberation in terms of the (DRR) in the context of the assessment of dereverberation algorithms for which we wish to quantify the level of reverberation before and after processing. The DRR is normally calculated from the
Patrick A. Naylor +2 more
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