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The Journal of the Acoustical Society of America, 2020
Blind source separation (BSS) has many applications: sound scene analysis, speech recognition, medical signal processing, etc. However, most of these applications concern the temporal separation of signals. Studies have shown the effectiveness of separation in the ambisonic domain with spherical microphone recordings.
Louis J. Dermagne +2 more
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Blind source separation (BSS) has many applications: sound scene analysis, speech recognition, medical signal processing, etc. However, most of these applications concern the temporal separation of signals. Studies have shown the effectiveness of separation in the ambisonic domain with spherical microphone recordings.
Louis J. Dermagne +2 more
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Sound source separation using convolutional mixing and a priori sound source knowledge
The Journal of the Acoustical Society of America, 2005Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source ...
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Low-latency sound source separation using deep neural networks
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016Sound source separation at low-latency requires that each incoming frame of audio data be processed at very low delay, and outputted as soon as possible. For practical purposes involving human listeners, a 20 ms algorithmic delay is the uppermost limit which is comfortable to the listener. In this paper, we propose a low-latency (algorithmic delay < 20
Gaurav Naithani +4 more
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Sound source separation and automatic speech recognition for moving sources
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010This paper addresses sound source separation and speech recognition for moving sound sources. Real-world applications such as robots should cope with both moving and stationary sound sources. However, most studies assume only stationary sound sources. We introduce three key techniques to cope with moving sources, that is, Adaptive Step-size control (AS)
Kazuhiro Nakadai +3 more
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Background-Sound Controllable Voice Source Separation
INTERSPEECH 2023, 2023Deokjun Eom, Woo Hyun Nam, Kyung-Rae Kim
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Joint Object Detection and Sound Source Separation
We propose See2Hear (S2H), a framework that jointly learns audio-visual representations for object detection and sound source separation from videos. Existing methods do not fully exploit the synergy between the detection and separation tasks, often relying on disjointly pre-trained visual encoders. In this paper, S2H integrates both tasks in an end-to-Sunyoo Kim +7 more
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Multi-sources separation for sound source localization
Interspeech 2014, 2014Mariem Bouafif, Zied Lachiri
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A survey of sound source localization with deep learning methods
Journal of the Acoustical Society of America, 2022Alexandre Guerin
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Deep learning-based method for multiple sound source localization with high resolution and accuracy
Mechanical Systems and Signal Processing, 2021Soo Young Lee
exaly