Artificial Intelligence
Making machines smarter for improved safety, efficiency and comfort.
Our AI research encompasses advances in computer vision, speech and audio processing, as well as data analytics. Key research themes include improved perception based on machine learning techniques, learning control policies through model-based reinforcement learning, as well as cognition and reasoning based on learned semantic representations. We apply our work to a broad range of automotive and robotics applications, as well as building and home systems.
Quick Links
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Researchers
Jonathan
Le Roux
Toshiaki
Koike-Akino
Ye
Wang
Gordon
Wichern
Anoop
Cherian
Chiori
Hori
Tim K.
Marks
Michael J.
Jones
Daniel N.
Nikovski
Kieran
Parsons
Devesh K.
Jha
François
Germain
Philip V.
Orlik
Suhas
Lohit
Matthew
Brand
Petros T.
Boufounos
Hassan
Mansour
Diego
Romeres
Pu
(Perry)
WangMoitreya
Chatterjee
Siddarth
Jain
William S.
Yerazunis
Sameer
Khurana
Mouhacine
Benosman
Zexu
Pan
Kuan-Chuan
Peng
Arvind
Raghunathan
Radu
Corcodel
Hongbo
Sun
Yebin
Wang
Jianlin
Guo
Chungwei
Lin
Jing
Liu
Yanting
Ma
Bingnan
Wang
Stefano
Di Cairano
Anthony
Vetro
Jinyun
Zhang
Jose
Amaya
Karl
Berntorp
Ankush
Chakrabarty
Vedang M.
Deshpande
Dehong
Liu
Wataru
Tsujita
Abraham P.
Vinod
Janek
Ebbers
Ryo
Hase
James
Queeney
Shinya
Tsuruta
Ryoma
Yataka
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Awards
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AWARD Jonathan Le Roux elevated to IEEE Fellow Date: January 1, 2024
Awarded to: Jonathan Le Roux
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL Distinguished Scientist and Speech & Audio Senior Team Leader Jonathan Le Roux has been elevated to IEEE Fellow, effective January 2024, "for contributions to multi-source speech and audio processing."
Mitsubishi Electric celebrated Dr. Le Roux's elevation and that of another researcher from the company, Dr. Shumpei Kameyama, with a worldwide news release on February 15.
Dr. Jonathan Le Roux has made fundamental contributions to the field of multi-speaker speech processing, especially to the areas of speech separation and multi-speaker end-to-end automatic speech recognition (ASR). His contributions constituted a major advance in realizing a practically usable solution to the cocktail party problem, enabling machines to replicate humans’ ability to concentrate on a specific sound source, such as a certain speaker within a complex acoustic scene—a long-standing challenge in the speech signal processing community. Additionally, he has made key contributions to the measures used for training and evaluating audio source separation methods, developing several new objective functions to improve the training of deep neural networks for speech enhancement, and analyzing the impact of metrics used to evaluate the signal reconstruction quality. Dr. Le Roux’s technical contributions have been crucial in promoting the widespread adoption of multi-speaker separation and end-to-end ASR technologies across various applications, including smart speakers, teleconferencing systems, hearables, and mobile devices.
IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society. Each year, following a rigorous evaluation procedure, the IEEE Fellow Committee recommends a select group of recipients for elevation to IEEE Fellow. Less than 0.1% of voting members are selected annually for this member grade elevation.
- MERL Distinguished Scientist and Speech & Audio Senior Team Leader Jonathan Le Roux has been elevated to IEEE Fellow, effective January 2024, "for contributions to multi-source speech and audio processing."
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AWARD Honorable Mention Award at NeurIPS 23 Instruction Workshop Date: December 15, 2023
Awarded to: Lingfeng Sun, Devesh K. Jha, Chiori Hori, Siddharth Jain, Radu Corcodel, Xinghao Zhu, Masayoshi Tomizuka and Diego Romeres
MERL Contacts: Radu Corcodel; Chiori Hori; Siddarth Jain; Devesh K. Jha; Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- MERL Researchers received an "Honorable Mention award" at the Workshop on Instruction Tuning and Instruction Following at the NeurIPS 2023 conference in New Orleans. The workshop was on the topic of instruction tuning and Instruction following for Large Language Models (LLMs). MERL researchers presented their work on interactive planning using LLMs for partially observable robotic tasks during the oral presentation session at the workshop.
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AWARD MERL team wins the Audio-Visual Speech Enhancement (AVSE) 2023 Challenge Date: December 16, 2023
Awarded to: Zexu Pan, Gordon Wichern, Yoshiki Masuyama, Francois Germain, Sameer Khurana, Chiori Hori, and Jonathan Le Roux
MERL Contacts: François Germain; Chiori Hori; Sameer Khurana; Jonathan Le Roux; Zexu Pan; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL's Speech & Audio team ranked 1st out of 12 teams in the 2nd COG-MHEAR Audio-Visual Speech Enhancement Challenge (AVSE). The team was led by Zexu Pan, and also included Gordon Wichern, Yoshiki Masuyama, Francois Germain, Sameer Khurana, Chiori Hori, and Jonathan Le Roux.
The AVSE challenge aims to design better speech enhancement systems by harnessing the visual aspects of speech (such as lip movements and gestures) in a manner similar to the brain’s multi-modal integration strategies. MERL’s system was a scenario-aware audio-visual TF-GridNet, that incorporates the face recording of a target speaker as a conditioning factor and also recognizes whether the predominant interference signal is speech or background noise. In addition to outperforming all competing systems in terms of objective metrics by a wide margin, in a listening test, MERL’s model achieved the best overall word intelligibility score of 84.54%, compared to 57.56% for the baseline and 80.41% for the next best team. The Fisher’s least significant difference (LSD) was 2.14%, indicating that our model offered statistically significant speech intelligibility improvements compared to all other systems.
- MERL's Speech & Audio team ranked 1st out of 12 teams in the 2nd COG-MHEAR Audio-Visual Speech Enhancement Challenge (AVSE). The team was led by Zexu Pan, and also included Gordon Wichern, Yoshiki Masuyama, Francois Germain, Sameer Khurana, Chiori Hori, and Jonathan Le Roux.
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News & Events
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EVENT MERL Contributes to ICASSP 2024 Date: Sunday, April 14, 2024 - Friday, April 19, 2024
Location: Seoul, South Korea
MERL Contacts: Petros T. Boufounos; François Germain; Chiori Hori; Sameer Khurana; Toshiaki Koike-Akino; Jonathan Le Roux; Hassan Mansour; Zexu Pan; Kieran Parsons; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern; Ryoma Yataka
Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Robotics, Signal Processing, Speech & AudioBrief- MERL has made numerous contributions to both the organization and technical program of ICASSP 2024, which is being held in Seoul, Korea from April 14-19, 2024.
Sponsorship and Awards
MERL is proud to be a Bronze Patron of the conference and will participate in the student job fair on Thursday, April 18. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.
MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Stéphane G. Mallat, the recipient of the 2024 IEEE Fourier Award for Signal Processing, and Prof. Keiichi Tokuda, the recipient of the 2024 IEEE James L. Flanagan Speech and Audio Processing Award.
Jonathan Le Roux, MERL Speech and Audio Senior Team Leader, will also be recognized during the Awards Ceremony for his recent elevation to IEEE Fellow.
Technical Program
MERL will present 13 papers in the main conference on a wide range of topics including automated audio captioning, speech separation, audio generative models, speech and sound synthesis, spatial audio reproduction, multimodal indoor monitoring, radar imaging, depth estimation, physics-informed machine learning, and integrated sensing and communications (ISAC). Three workshop papers have also been accepted for presentation on audio-visual speaker diarization, music source separation, and music generative models.
Perry Wang is the co-organizer of the Workshop on Signal Processing and Machine Learning Advances in Automotive Radars (SPLAR), held on Sunday, April 14. It features keynote talks from leaders in both academia and industry, peer-reviewed workshop papers, and lightning talks from ICASSP regular tracks on signal processing and machine learning for automotive radar and, more generally, radar perception.
Gordon Wichern will present an invited keynote talk on analyzing and interpreting audio deep learning models at the Workshop on Explainable Machine Learning for Speech and Audio (XAI-SA), held on Monday, April 15. He will also appear in a panel discussion on interpretable audio AI at the workshop.
Perry Wang also co-organizes a two-part special session on Next-Generation Wi-Fi Sensing (SS-L9 and SS-L13) which will be held on Thursday afternoon, April 18. The special session includes papers on PHY-layer oriented signal processing and data-driven deep learning advances, and supports upcoming 802.11bf WLAN Sensing Standardization activities.
Petros Boufounos is participating as a mentor in ICASSP’s Micro-Mentoring Experience Program (MiME).
About ICASSP
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 3000 participants.
- MERL has made numerous contributions to both the organization and technical program of ICASSP 2024, which is being held in Seoul, Korea from April 14-19, 2024.
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TALK [MERL Seminar Series 2024] Melanie Mitchell presents talk titled "The Debate Over 'Understanding' in AI's Large Language Models" Date & Time: Tuesday, February 13, 2024; 1:00 PM
Speaker: Melanie Mitchell, Santa Fe Institute
MERL Host: Suhas Lohit
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Human-Computer InteractionAbstract- I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social situations language encodes -- in any important sense. I will describe arguments that have been made for and against such understanding, and, more generally, will discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems. I will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions.
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Research Highlights
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Internships
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ST2083: Deep Learning for Radar Perception
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.
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OR2110: Shared Autonomy for Human-Robot Interaction
MERL is looking for a highly motivated and qualified intern to work on human-robot interaction (HRI) research. The ideal candidate would be a Ph.D. student with a strong background in HRI, focusing on robotic manipulation, deep learning, probabilistic modeling, or reinforcement learning. Several topics are available for consideration, including Intent Recognition in Multi-Object Scenes, Shared Autonomy, Cooperative Manipulation, Human-Robot Handovers, and Representation Learning for HRI. Experience working with robotics hardware and physics engine simulators like PyBullet, Issac Gym, or Mujoco is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.
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ST2082: Integrated Sensing and Communication (ISAC)
The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in integrated sensing and communication (ISAC) with a focus on signal processing, model-based learning, and optimization. Expertise in joint waveform/sequence optimization, integrated ISAC precoder/combiner design, model-based learning for ISAC, and downlink/uplink/active sensing under timing and frequency offsets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus but not required. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for publication. The expected duration of the internship is 3 months with a flexible start date.
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Recent Publications
- "Late Audio-Visual Fusion for In-The-Wild Speaker Diarization", Hands-free Speech Communication and Microphone Arrays (HSCMA), April 2024.BibTeX TR2024-029 PDF
- @inproceedings{Pan2024apr,
- author = {Pan, Zexu and Wichern, Gordon and Germain, François G and Subramanian, Aswin and Le Roux, Jonathan},
- title = {Late Audio-Visual Fusion for In-The-Wild Speaker Diarization},
- booktitle = {Hands-free Speech Communication and Microphone Arrays (HSCMA)},
- year = 2024,
- month = apr,
- url = {https://www.merl.com/publications/TR2024-029}
- }
, - "Generation or Replication: Auscultating Audio Latent Diffusion Models", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-027 PDF
- @inproceedings{Bralios2024mar,
- author = {Bralios, Dimitrios and Wichern, Gordon and Germain, François G and Pan, Zexu and Khurana, Sameer and Hori, Chiori and Le Roux, Jonathan},
- title = {Generation or Replication: Auscultating Audio Latent Diffusion Models},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-027}
- }
, - "Why does music source separation benefit from cacophony?", IEEE ICASSP Satellite Workshop on Explainable Machine Learning for Speech and Audio (XAI-SA), March 2024.BibTeX TR2024-030 PDF
- @inproceedings{Jeon2024mar,
- author = {Jeon, Chang-Bin and Wichern, Gordon and Germain, François G and Le Roux, Jonathan},
- title = {Why does music source separation benefit from cacophony?},
- booktitle = {IEEE ICASSP Satellite Workshop on Explainable Machine Learning for Speech and Audio (XAI-SA)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-030}
- }
, - "NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-026 PDF
- @inproceedings{Masuyama2024mar,
- author = {Masuyama, Yoshiki and Wichern, Gordon and Germain, François G and Pan, Zexu and Khurana, Sameer and Hori, Chiori and Le Roux, Jonathan},
- title = {NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-026}
- }
, - "NeuroHeed+: Improving Neuro-steered Speaker Extraction with Joint Auditory Attention Detection", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-025 PDF
- @inproceedings{Pan2024mar,
- author = {Pan, Zexu and Wichern, Gordon and Germain, François G and Khurana, Sameer and Le Roux, Jonathan},
- title = {NeuroHeed+: Improving Neuro-steered Speaker Extraction with Joint Auditory Attention Detection},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-025}
- }
, - "Improving Audio Captioning Models with Fine-grained Audio Features, Text Embedding Supervision, and LLM Mix-up Augmentation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-028 PDF
- @inproceedings{Wu2024mar,
- author = {Wu, Shih-Lun and Chang, Xuankai and Wichern, Gordon and Jung, Jee-weon and Germain, François G and Le Roux, Jonathan and Watanabe, Shinji},
- title = {Improving Audio Captioning Models with Fine-grained Audio Features, Text Embedding Supervision, and LLM Mix-up Augmentation},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-028}
- }
, - "SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-013 PDF
- @inproceedings{Baoueb2024mar,
- author = {Baoueb, Teysir and Liu, Haocheng and Fontaine, Mathieu and Le Roux, Jonathan and Richard, Gaël},
- title = {SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-013}
- }
, - "Wi-Fi based Indoor Monitoring Enhanced by Multimodal Fusion", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-012 PDF
- @inproceedings{Hori2024mar,
- author = {Hori, Chiori and Wang, Pu and Rahman, Mahbub and Vaca-Rubio, Cristian and Khurana, Sameer and Cherian, Anoop and Le Roux, Jonathan},
- title = {Wi-Fi based Indoor Monitoring Enhanced by Multimodal Fusion},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-012}
- }
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- "Late Audio-Visual Fusion for In-The-Wild Speaker Diarization", Hands-free Speech Communication and Microphone Arrays (HSCMA), April 2024.
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Videos
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Software & Data Downloads
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neural-IIR-field -
Pixel-Grounded Prototypical Part Networks -
DeepBornFNO -
Hyperbolic Audio Source Separation -
Simple Multimodal Algorithmic Reasoning Task Dataset -
SOurce-free Cross-modal KnowledgE Transfer -
Audio-Visual-Language Embodied Navigation in 3D Environments -
Nonparametric Score Estimators -
Instance Segmentation GAN -
Audio Visual Scene-Graph Segmentor -
Generalized One-class Discriminative Subspaces -
Goal directed RL with Safety Constraints -
Hierarchical Musical Instrument Separation -
Generating Visual Dynamics from Sound and Context -
Adversarially-Contrastive Optimal Transport -
Online Feature Extractor Network -
MotionNet -
FoldingNet++ -
Quasi-Newton Trust Region Policy Optimization -
Landmarks’ Location, Uncertainty, and Visibility Likelihood -
Robust Iterative Data Estimation -
Gradient-based Nikaido-Isoda -
Discriminative Subspace Pooling -
Partial Group Convolutional Neural Networks
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