Speech & Audio
Audio source separation, recognition, and understanding.
Our current research focuses on application of machine learning to estimation and inference problems in speech and audio processing. Topics include end-to-end speech recognition and enhancement, acoustic modeling and analysis, statistical dialog systems, as well as natural language understanding and adaptive multimodal interfaces.
Quick Links
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Researchers
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Awards
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AWARD Best Poster Award and Best Video Award at the International Society for Music Information Retrieval Conference (ISMIR) 2020 Date: October 15, 2020
Awarded to: Ethan Manilow, Gordon Wichern, Jonathan Le Roux
MERL Contacts: Jonathan Le Roux; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- Former MERL intern Ethan Manilow and MERL researchers Gordon Wichern and Jonathan Le Roux won Best Poster Award and Best Video Award at the 2020 International Society for Music Information Retrieval Conference (ISMIR 2020) for the paper "Hierarchical Musical Source Separation". The conference was held October 11-14 in a virtual format. The Best Poster Awards and Best Video Awards were awarded by popular vote among the conference attendees.
The paper proposes a new method for isolating individual sounds in an audio mixture that accounts for the hierarchical relationship between sound sources. Many sounds we are interested in analyzing are hierarchical in nature, e.g., during a music performance, a hi-hat note is one of many such hi-hat notes, which is one of several parts of a drumkit, itself one of many instruments in a band, which might be playing in a bar with other sounds occurring. Inspired by this, the paper re-frames the audio source separation problem as hierarchical, combining similar sounds together at certain levels while separating them at other levels, and shows on a musical instrument separation task that a hierarchical approach outperforms non-hierarchical models while also requiring less training data. The paper, poster, and video can be seen on the paper page on the ISMIR website.
- Former MERL intern Ethan Manilow and MERL researchers Gordon Wichern and Jonathan Le Roux won Best Poster Award and Best Video Award at the 2020 International Society for Music Information Retrieval Conference (ISMIR 2020) for the paper "Hierarchical Musical Source Separation". The conference was held October 11-14 in a virtual format. The Best Poster Awards and Best Video Awards were awarded by popular vote among the conference attendees.
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AWARD Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019 Date: December 18, 2019
Awarded to: Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL researcher Jonathan Le Roux and co-authors Xuankai Chang, Shinji Watanabe (Johns Hopkins University), Wangyou Zhang, and Yanmin Qian (Shanghai Jiao Tong University) won the Best Paper Award at the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), for the paper "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition". MIMO-Speech is a fully neural end-to-end framework that can transcribe the text of multiple speakers speaking simultaneously from multi-channel input. The system is comprised of a monaural masking network, a multi-source neural beamformer, and a multi-output speech recognition model, which are jointly optimized only via an automatic speech recognition (ASR) criterion. The award was received by lead author Xuankai Chang during the conference, which was held in Sentosa, Singapore from December 14-18, 2019.
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AWARD Best Student Paper Award at IEEE ICASSP 2018 Date: April 17, 2018
Awarded to: Zhong-Qiu Wang
MERL Contact: Jonathan Le Roux
Research Area: Speech & AudioBrief- Former MERL intern Zhong-Qiu Wang (Ph.D. Candidate at Ohio State University) has received a Best Student Paper Award at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018) for the paper "Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation" by Zhong-Qiu Wang, Jonathan Le Roux, and John Hershey. The paper presents work performed during Zhong-Qiu's internship at MERL in the summer 2017, extending MERL's pioneering Deep Clustering framework for speech separation to a multi-channel setup. The award was received on behalf on Zhong-Qiu by MERL researcher and co-author Jonathan Le Roux during the conference, held in Calgary April 15-20.
See All Awards for Speech & Audio -
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News & Events
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NEWS MERL launches Postdoctoral Research Fellow program Date: September 21, 2022
MERL Contacts: Philip V. Orlik; Anthony Vetro
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & AudioBrief- Mitsubishi Electric Research Laboratories (MERL) invites qualified postdoctoral candidates to apply for the position of Postdoctoral Research Fellow. This position provides early career scientists the opportunity to work at a unique, academically-oriented industrial research laboratory. Successful candidates will be expected to define and pursue their own original research agenda, explore connections to established laboratory initiatives, and publish high impact articles in leading venues. Please refer to our web page for further details.
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TALK [MERL Seminar Series 2022] Prof. Chuang Gan presents talk titled Learning to Perceive Physical Scenes from Multi-Sensory Data Date & Time: Tuesday, September 6, 2022; 12:00 PM EDT
Speaker: Chuang Gan, UMass Amherst & MIT-IBM Watson AI Lab
MERL Host: Jonathan Le Roux
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & AudioAbstract
Human sensory perception of the physical world is rich and multimodal and can flexibly integrate input from all five sensory modalities -- vision, touch, smell, hearing, and taste. However, in AI, attention has primarily focused on visual perception. In this talk, I will introduce my efforts in connecting vision with sound, which will allow machine perception systems to see objects and infer physics from multi-sensory data. In the first part of my talk, I will introduce a. self-supervised approach that could learn to parse images and separate the sound sources by watching and listening to unlabeled videos without requiring additional manual supervision. In the second part of my talk, I will show we may further infer the underlying causal structure in 3D environments through visual and auditory observations. This enables agents to seek the sound source of repeating environmental sound (e.g., alarm) or identify what object has fallen, and where, from an intermittent impact sound.
See All News & Events for Speech & Audio -
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Research Highlights
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Internships
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SA1874: Audio source separation and sound event detection
We are seeking graduate students interested in helping advance the fields of source separation, speech enhancement, robust ASR, and sound event detection/localization in challenging multi-source and far-field scenarios. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, sequence to sequence models, and deep learning techniques, in particular those involving minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few-shot learning). Multiple positions are available throughout 2023 (Spring/Summer of course, but also as early as January), with expected durations of 3-6 months and flexible start dates.
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Openings
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Recent Publications
- , "Momentum Pseudo-Labeling: Semi-Supervised ASR with Continuously Improving Pseudo-Labels", IEEE Journal of Selected Topics in Signal Processing, September 2022.BibTeX TR2022-112 PDF
- @article{Higuchi2022sep,
- author = {Higuchi, Yosuke and Moritz, Niko and Le Roux, Jonathan and Hori, Takaaki},
- title = {Momentum Pseudo-Labeling: Semi-Supervised ASR with Continuously Improving Pseudo-Labels},
- journal = {IEEE Journal of Selected Topics in Signal Processing},
- year = 2022,
- month = sep,
- url = {https://www.merl.com/publications/TR2022-112}
- }
- , "Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection," Tech. Rep. TR2022-092, Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2022, July 2022.BibTeX TR2022-092 PDF
- @techreport{Venkatesh2022jul,
- author = {Venkatesh, Satvik and Wichern, Gordon and Subramanian, Aswin Shanmugam and Le Roux, Jonathan},
- title = {Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection},
- institution = {DCASE2022 Challenge},
- year = 2022,
- month = jul,
- url = {https://www.merl.com/publications/TR2022-092}
- }
- , "Quantifying Predictive Uncertainty for Stochastic Video Synthesis from Audio", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.BibTeX TR2022-082 PDF
- @inproceedings{Chatterjee2022jun,
- author = {Chatterjee, Moitreya and Ahuja, Narendra and Cherian, Anoop},
- title = {Quantifying Predictive Uncertainty for Stochastic Video Synthesis from Audio},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-082}
- }
- , "Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP43922.2022.9747375, April 2022, pp. 7322-7326.BibTeX TR2022-021 PDF
- @inproceedings{Chang2022apr,
- author = {Chang, Xuankai and Moritz, Niko and Hori, Takaaki and Watanabe, Shinji and Le Roux, Jonathan},
- title = {Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- pages = {7322--7326},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP43922.2022.9747375},
- url = {https://www.merl.com/publications/TR2022-021}
- }
- , "Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP43922.2022.9746275, April 2022, pp. 7672-7676.BibTeX TR2022-026 PDF
- @inproceedings{Higuchi2022apr,
- author = {Higuchi, Yosuke and Moritz, Niko and Le Roux, Jonathan and Hori, Takaaki},
- title = {Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- pages = {7672--7676},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP43922.2022.9746275},
- url = {https://www.merl.com/publications/TR2022-026}
- }
- , "Sequence Transduction with Graph-based Supervision", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP43922.2022.9747788, April 2022, pp. 7212-7216.BibTeX TR2022-024 PDF
- @inproceedings{Moritz2022apr,
- author = {Moritz, Niko and Hori, Takaaki and Watanabe, Shinji and Le Roux, Jonathan},
- title = {Sequence Transduction with Graph-based Supervision},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- pages = {7212--7216},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP43922.2022.9747788},
- url = {https://www.merl.com/publications/TR2022-024}
- }
- , "The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP43922.2022.9746005, April 2022, pp. 526-530.BibTeX TR2022-022 PDF
- @inproceedings{Petermann2022apr,
- author = {Petermann, Darius and Wichern, Gordon and Wang, Zhong-Qiu and Le Roux, Jonathan},
- title = {The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- pages = {526--530},
- month = apr,
- doi = {10.1109/ICASSP43922.2022.9746005},
- url = {https://www.merl.com/publications/TR2022-022}
- }
- , "Audio-Visual Scene-Aware Dialog and Reasoning Using Audio-Visual Transformers with Joint Student-Teacher Learning", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2022, pp. 7732-7736.BibTeX TR2022-019 PDF
- @inproceedings{Shah2022apr,
- author = {Shah, Ankit Parag and Geng, Shijie and Gao, Peng and Cherian, Anoop and Hori, Takaaki and Marks, Tim K. and Le Roux, Jonathan and Hori, Chiori},
- title = {Audio-Visual Scene-Aware Dialog and Reasoning Using Audio-Visual Transformers with Joint Student-Teacher Learning},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- pages = {7732--7736},
- month = apr,
- publisher = {IEEE},
- issn = {1520-6149},
- isbn = {978-1-6654-0540-9},
- url = {https://www.merl.com/publications/TR2022-019}
- }
- , "Momentum Pseudo-Labeling: Semi-Supervised ASR with Continuously Improving Pseudo-Labels", IEEE Journal of Selected Topics in Signal Processing, September 2022.
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Videos
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[MERL Seminar Series Spring 2022] Learning Speech Representations with Multimodal Self-Supervision
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The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
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[MERL Seminar Series 2021] Look and Listen: From Semantic to Spatial Audio-Visual Perception
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Scene-Aware Interaction Technology
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Seamless Speech Recognition
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Speech Enhancement and Separation
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Software Downloads
