scholarly journals A multichannel learning-based approach for sound source separation in reverberant environments

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
You-Siang Chen ◽  
Zi-Jie Lin ◽  
Mingsian R. Bai
Keyword(s):  
Frequency Domain ◽  
Sound Source ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Objective Evaluation ◽  
Linear Mapping ◽  
Invariant Mean ◽  
Scale Invariant ◽  
Sound Source Separation ◽  
Reverberant Field

AbstractIn 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. In the first stage, time-dilated convolutional blocks are trained to estimate the array weights for beamforming the multichannel microphone signals. Next, the output of the network is processed by a weight-and-sum operation that is reformulated to handle real-valued data in the frequency domain. In the second stage, a U-net model is concatenated to the beamforming network to serve as a non-linear mapping filter for joint separation and dereverberation. The scale invariant mean square error (SI-MSE) that is a frequency-domain modification from the scale invariant signal-to-noise ratio (SI-SNR) is used as the objective function for training. Furthermore, the combined network is also trained with the speech segments filtered by a great variety of room impulse responses. Simulations are conducted for comprehensive multisource scenarios of various subtending angles of sources and reverberation times. The proposed network is compared with several baseline approaches in terms of objective evaluation matrices. The results have demonstrated the excellent performance of the proposed network in dereverberation and separation, as compared to baseline methods.

scholarly journals Sound Source Separation Mechanisms of Different Deep Networks Explained from the Perspective of Auditory Perception

2022 ◽  
Vol 12 (2) ◽  
pp. 832
Author(s):  
Han Li ◽  
Kean Chen ◽  
Lei Wang ◽  
Jianben Liu ◽  
Baoquan Wan ◽  
...  
Keyword(s):  
Sound Source ◽  
Auditory Perception ◽  
Source Separation ◽  
Frequency Resolution ◽  
Scale Invariant ◽  
Universal System ◽  
Separation Mechanisms ◽  
Sound Source Separation ◽  
Deep Networks ◽  
Proximity Principle

Thanks to the development of deep learning, various sound source separation networks have been proposed and made significant progress. However, the study on the underlying separation mechanisms is still in its infancy. In this study, deep networks are explained from the perspective of auditory perception mechanisms. For separating two arbitrary sound sources from monaural recordings, three different networks with different parameters are trained and achieve excellent performances. The networks’ output can obtain an average scale-invariant signal-to-distortion ratio improvement (SI-SDRi) higher than 10 dB, comparable with the human performance to separate natural sources. More importantly, the most intuitive principle—proximity—is explored through simultaneous and sequential organization experiments. Results show that regardless of network structures and parameters, the proximity principle is learned spontaneously by all networks. If components are proximate in frequency or time, they are not easily separated by networks. Moreover, the frequency resolution at low frequencies is better than at high frequencies. These behavior characteristics of all three networks are highly consistent with those of the human auditory system, which implies that the learned proximity principle is not accidental, but the optimal strategy selected by networks and humans when facing the same task. The emergence of the auditory-like separation mechanisms provides the possibility to develop a universal system that can be adapted to all sources and scenes.

scholarly journals Sound Source Separation in the Frequency Domain with Image Processing

2001 ◽  
Vol 121 (12) ◽  
pp. 1866-1874 ◽  
Author(s):  
Kazuhiro Ninagawa ◽  
Takashi Umeyama ◽  
Kenji Suzuki ◽  
Noboru Sugie
Keyword(s):  
Image Processing ◽  
Frequency Domain ◽  
Sound Source ◽  
Source Separation ◽  
Sound Source Separation

Target Detection in a Scenario of Natural Sounds: The Role of Spatial Sound Source Separation

2013 ◽  
Author(s):  
Susanne Mayr ◽  
Gunnar Regenbrecht ◽  
Kathrin Lange ◽  
Albertgeorg Lang ◽  
Axel Buchner
Keyword(s):  
Target Detection ◽  
Sound Source ◽  
Source Separation ◽  
Natural Sounds ◽  
Sound Source Separation ◽  
Spatial Sound

scholarly journals Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 532
Author(s):  
Henglin Pu ◽  
Chao Cai ◽  
Menglan Hu ◽  
Tianping Deng ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Indoor Localization ◽  
Weighting Function ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Sound Source Localization ◽  
Angle Of Arrival ◽  
Sound Sources ◽  
Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

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