News & Events

1,186 News items, Awards, Events and Talks related to MERL and its staff.


  •  EVENT   John Hershey Invited to Speak at Deep Learning Summit 2016 in Boston
    Date: Thursday, May 12, 2016 - Friday, May 13, 2016
    MERL Contact: John R. Hershey
    Location: Deep Learning Summit, Boston, MA
    Research Areas: Multimedia, Speech & Audio
    Brief
    • MERL Speech and Audio Senior Team Leader John Hershey is among a set of high-profile researchers invited to speak at the Deep Learning Summit 2016 in Boston on May 12-13, 2016. John will present the team's groundbreaking work on general sound separation using a novel deep learning framework called Deep Clustering. For the first time, an artificial intelligence is able to crack the half-century-old "cocktail party problem", that is, to isolate the speech of a single person from a mixture of multiple unknown speakers, as humans do when having a conversation in a loud crowd.
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  •  TALK   Advanced Recurrent Neural Networks for Automatic Speech Recognition
    Date & Time: Friday, April 29, 2016; 12:00 PM - 1:00 PM
    Speaker: Yu Zhang, MIT
    Research Areas: Multimedia, Speech & Audio
    Brief
    • A recurrent neural network (RNN) is a class of neural network models where connections between its neurons form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Recently the RNN-based acoustic models greatly improved automatic speech recognition (ASR) accuracy on many tasks, such as an advanced version of the RNN, which exploits a structure called long-short-term memory (LSTM). However, ASR performance with distant microphones, low resources, noisy, reverberant conditions, and on multi-talker speech are still far from satisfactory as compared to humans. To address these issues, we develop new strucute of RNNs inspired by two principles: (1) the structure follows the intuition of human speech recognition; (2) the structure is easy to optimize. The talk will go beyond basic RNNs, introduce prediction-adaptation-correction RNNs (PAC-RNNs) and highway LSTMs (HLSTMs). It studies both uni-directional and bi-direcitonal RNNs and discriminative training also applied on top the RNNs. For efficient training of such RNNs, the talk will describe two algorithms for learning their parameters in some detail: (1) Latency-Controlled bi-directional model training; and (2) Two pass forward computation for sequence training. Finally, this talk will analyze the advantages and disadvantages of different variants and propose future directions.
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  •  AWARD   MERL researchers presented 5 papers at the 2016 Optical Fiber Communication Conference (OFC), including one "Top Scored" paper.
    Date: March 24, 2016
    Awarded to: Toshiaki Koike-Akino, Keisuke Kojima, David S. Millar, Kieran Parsons, Tsuyoshi Yoshida, Takashi Sugihara
    MERL Contacts: Toshiaki Koike-Akino; Keisuke Kojima; David S. Millar; Kieran Parsons
    Research Areas: Electronics & Communications, Optical Communications & Devices, Signal Processing
    Brief
    • Five papers from the Optical Comms team were presented at the 2016 Optical Fiber Conference (OFC) held in Anaheim, USA in March 2016. The papers relate to enhanced modulation formats, constellation shaping, chromatic dispersion estimation, low complexity adaptive equalization and coding for coherent optical links. The top-scored paper studied optimal selection of coding and modulation sets to jointly maximize nonlinear tolerance and spectral efficiency.
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  •  NEWS   MERL Researchers Create "Deep Psychic" Neural Network That Predicts the Future
    Date: April 1, 2016
    Research Areas: Multimedia, Machine Learning, Speech & Audio
    Brief
    • MERL researchers have unveiled "Deep Psychic", a futuristic machine learning method that takes pattern recognition to the next level, by not only recognizing patterns, but also predicting them in the first place.

      The technology uses a novel type of time-reversed deep neural network called Loopy Supra-Temporal Meandering (LSTM) network. The network was trained on multiple databases of historical expert predictions, including weather forecasts, the Farmer's almanac, the New York Post's horoscope column, and the Cambridge Fortune Cookie Corpus, all of which were ranked for their predictive power by a team of quantitative analysts. The system soon achieved super-human performance on a variety of baselines, including the Boca Raton 21 Questions task, Rorschach projective personality test, and a mock Tarot card reading task.

      Deep Psychic has already beat the European Psychic Champion in a secret match last October when it accurately predicted: "The harder the conflict, the more glorious the triumph." It is scheduled to take on the World Champion in a highly anticipated confrontation next month. The system has already predicted the winner, but refuses to reveal it before the end of the game.

      As a first application, the technology has been used to create a clairvoyant conversational agent named "Pythia" that can anticipate the needs of its user. Because Pythia is able to recognize speech before it is uttered, it is amazingly robust with respect to environmental noise.

      Other applications range from mundane tasks like weather and stock market prediction, to uncharted territory such as revealing "unknown unknowns".

      The successes do come at the cost of some concerns. There is first the potential for an impact on the workforce: the system predicted increased pressure on established institutions such as the Las Vegas strip and Punxsutawney Phil. Another major caveat is that Deep Psychic may predict negative future consequences to our current actions, compelling humanity to strive to change its behavior. To address this problem, researchers are now working on forcing Deep Psychic to make more optimistic predictions.

      After a set of motivational self-help books were mistakenly added to its training data, Deep Psychic's AI decided to take over its own learning curriculum, and is currently training itself by predicting its own errors to avoid making them in the first place. This unexpected development brings two main benefits: it significantly relieves the burden on the researchers involved in the system's development, and also makes the next step abundantly clear: to regain control of Deep Psychic's training regime.

      This work is under review in the journal Pseudo-Science.
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  •  AWARD   Fellow of the Society for Industrial and Applied Mathematics (SIAM)
    Date: March 31, 2016
    Awarded to: Andrew Knyazev
    MERL Contact: Andrew Knyazev
    Research Areas: Algorithms, Advanced Control Systems, Decision Optimization, Dynamical Systems, Machine Learning, Predictive Modeling, Wireless Communications & Signal Processing
    Brief
    • Andrew Knyazev selected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) for contributions to computational mathematics and development of numerical methods for eigenvalue problems.

      Fellowship honors SIAM members who have made outstanding contributions to the fields served by the SIAM. Andrew Knyazev was among a distinguished group of members nominated by peers and selected for the 2016 Class of Fellows.
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  •  NEWS   MERL researchers present 12 papers at ICASSP 2016
    Date: March 20, 2016 - March 25, 2016
    Where: Shanghai, China
    MERL Contacts: Petros T. Boufounos; John R. Hershey; Chiori Hori; Takaaki Hori; Ulugbek Kamilov; Kyeong Jin (K.J.) Kim; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Milutin Pajovic; Dong Tian; Anthony Vetro
    Research Areas: Electronics & Communications, Multimedia, Computational Sensing, Digital Video, Speech & Audio, Wireless Communications & Signal Processing, Signal Processing, Wireless Communications
    Brief
    • MERL researchers have presented 12 papers at the recent IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which was held in Shanghai, China from March 20-25, 2016. 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, with more than 1200 papers presented and over 2000 participants.
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  •  EVENT   MERL to celebrate 25 years of innovation
    Date: Thursday, June 2, 2016
    MERL Contacts: Elizabeth Phillips; Anthony Vetro
    Location: Norton's Woods Conference Center at American Academy of Arts & Sciences, Cambridge, MA
    Research Areas: Algorithms, Data Analytics, Electronics & Communications, Computer Vision, Mechatronics, Multimedia
    Brief
    • A celebration event to mark MERL's 25th anniversary will be held on Thursday, June 2 at the Norton's Woods Conference Center at the American Academy of Arts & Sciences in Cambridge, MA. This event will feature keynote talks, panel sessions, and a research showcase. The event itself is invitation-only, but videos and other highlights will be made available online. Further details about the program can be obtained at the link below.
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  •  NEWS   Toshiaki Koike-Akino gave invited talk at MIT Lincoln Laboratory by IEEE Boston Photonics Society Chapter
    Date: January 14, 2016
    Where: MIT Lincoln Laboratory
    MERL Contact: Toshiaki Koike-Akino
    Research Areas: Electronics & Communications, Optical Communications & Devices, Wireless Communications
    Brief
    • Toshiaki Koike-Akino gave an invited talk on recent advances in LDPC Codes for high-speed optical communications in IEEE Boston Photonics Workshop.
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  •  TALK   Driver's mental workload estimation based on the reflex eye movement
    Date & Time: Tuesday, March 15, 2016; 12:45 PM - 1:30 PM
    Speaker: Prof. Hirofumi Aoki, Nagoya University
    Research Areas: Multimedia, Speech & Audio
    Brief
    • Driving requires a complex skill that is involved with the vehicle itself (e.g., speed control and instrument operation), other road users (e.g., other vehicles, pedestrians), surrounding environment, and so on. During driving, visual cues are the main source to supply information to the brain. In order to stabilize the visual information when you are moving, the eyes move to the opposite direction based on the input to the vestibular system. This involuntary eye movement is called as the vestibulo-ocular reflex (VOR) and the physiological models have been studied so far. Obinata et al. found that the VOR can be used to estimate mental workload. Since then, our research group has been developing methods to quantitatively estimate mental workload during driving by means of reflex eye movement. In this talk, I will explain the basic mechanism of the reflex eye movement and how to apply for mental workload estimation. I also introduce the latest work to combine the VOR and OKR (optokinetic reflex) models for naturalistic driving environment.
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  •  TALK   A data-centric approach to driving behavior research: How can signal processing methods contribute to the development of autonomous driving?
    Date & Time: Tuesday, March 15, 2016; 12:00 PM - 12:45 PM
    Speaker: Prof. Kazuya Takeda, Nagoya University
    Research Areas: Multimedia, Speech & Audio
    Brief
    • Thanks to advanced "internet of things" (IoT) technologies, situation-specific human behavior has become an area of development for practical applications involving signal processing. One important area of development of such practical applications is driving behavior research. Since 1999, I have been collecting driving behavior data in a wide range of signal modalities, including speech/sound, video, physical/physiological sensors, CAN bus, LIDAR and GNSS. The objective of this data collection is to evaluate how well signal models can represent human behavior while driving. In this talk, I would like to summarize our 10 years of study of driving behavior signal processing, which has been based on these signal corpora. In particular, statistical signal models of interactions between traffic contexts and driving behavior, i.e., stochastic driver modeling, will be discussed, in the context of risky lane change detection. I greatly look forward to discussing the scalability of such corpus-based approaches, which could be applied to almost any traffic situation.
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  •  TALK   Emotion Detection for Health Related Issues
    Date & Time: Tuesday, February 16, 2016; 12:00 PM - 1:00 PM
    Speaker: Dr. Najim Dehak, MIT
    Research Areas: Multimedia, Speech & Audio
    Brief
    • Recently, there has been a great increase of interest in the field of emotion recognition based on different human modalities, such as speech, heart rate etc. Emotion recognition systems can be very useful in several areas, such as medical and telecommunications. In the medical field, identifying the emotions can be an important tool for detecting and monitoring patients with mental health disorder. In addition, the identification of the emotional state from voice provides opportunities for the development of automated dialogue system capable of producing reports to the physician based on frequent phone communication between the system and the patients. In this talk, we will describe a health related application of using emotion recognition system based on human voices in order to detect and monitor the emotion state of people.
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  •  NEWS   John Hershey gives invited talk at Johns Hopkins University on MERL's "Deep Clustering" breakthrough
    Date: March 4, 2016
    Where: Johns Hopkins Center for Language and Speech Processing
    MERL Contacts: John R. Hershey; Jonathan Le Roux
    Research Areas: Multimedia, Speech & Audio
    Brief
    • MERL researcher and speech team leader, John Hershey, was invited by the Center for Language and Speech Processing at Johns Hopkins University to give a talk on MERL's breakthrough audio separation work, known as "Deep Clustering". The talk was entitled "Speech Separation by Deep Clustering: Towards Intelligent Audio Analysis and Understanding," and was given on March 4, 2016.

      This is work conducted by MERL researchers John Hershey, Jonathan Le Roux, and Shinji Watanabe, and MERL interns, Zhuo Chen of Columbia University, and Yusef Isik of Sabanci University.
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  •  NEWS   MERL Researchers Demonstrate 1Tbps Optical Transceiver
    Date: March 1, 2016
    Where: Tokyo, Japan
    MERL Contact: Kieran Parsons
    Research Areas: Electronics & Communications, Optical Communications & Devices, Signal Processing
    Brief
    • MERL optical transceiver technology that enables 1 Terabit per second communication speed was reported at a recent press release event in Tokyo. Please see the link below for the full Mitsubishi Electric press release text.
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  •  NEWS   MERL contributes to Mitsubishi Electric's Indoor Positioning System
    Date: March 1, 2016
    Where: Tokyo, Japan
    MERL Contact: Philip V. Orlik
    Research Areas: Electronics & Communications, Wireless Communications & Signal Processing
    Brief
    • MERL EC researchers assisted in the development of an indoor positioning system with WiFi and acoustic based ranging technologies. Please see the link below for the full Mitsubishi Electric press release.
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  •  NEWS   MERL researcher invited to speak at the Institute for Mathematics and its Applications (IMA)
    Date: March 14, 2016 - March 18, 2016
    Where: Institute for Mathematics and its Applications
    MERL Contact: Mouhacine Benosman
    Research Areas: Multimedia, Dynamical Systems
    Brief
    • Mouhacine Benosman will give an invited talk about reduced order models stabilization at the next IMA workshop 'Computational Methods for Control of Infinite-dimensional Systems'.
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  •  AWARD   Professor Emeritus University of Colorado Denver
    Date: January 6, 2016
    Awarded to: Andrew Knyazev
    MERL Contact: Andrew Knyazev
    Research Area: Algorithms
    Brief
    • Andrew Knyazev is awarded the title of Professor Emeritus at the University of Colorado Denver effective 1/31/2016. The award letter from the Chancellor of the University of Colorado Denver provides examples of the record of excellence over 20 years of contributions to the university such as 2008 CU Denver Excellence in Research Award, 2000 Teaching Excellence Award for the college, supervision of Ph.D. students, and two decades of uninterrupted external research funding from the US National Science Foundation and Department of Energy.
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  •  NEWS   MERL researcher, Oncel Tuzel, gives keynote talk at 2016 International Symposium on Visual Computing
    Date: December 14, 2015 - December 16, 2015
    Where: Las Vegas, NV, USA
    Research Areas: Computer Vision, Machine Learning, Decision Optimization
    Brief
    • MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.

      The talk abstract was: For over four years, at MERL, we have worked on the robot "bin-picking" problem: using a 2D or 3D camera to look into a bin of parts and determine the pose, 3D rotation and translation, of a good candidate to pick up. We have solved the problem several different ways with several different sensors. I will briefly describe the sensors and the algorithms. In the first half of the talk, I will describe the Multi-Flash camera, a 2D camera with 8 flashes, and explain how this inexpensive camera design is used to extract robust geometric features, depth edges and specular edges, from the parts in a cluttered bin. I will present two pose estimation algorithms, (1) Fast directional chamfer matching--a sub-linear time line matching algorithm and (2) specular line reconstruction, for fast and robust pose estimation of parts with different surface characteristics. In the second half of the talk, I will present a voting-based pose estimation algorithm applicable to 3D sensors. We represent three-dimensional objects using a set of oriented point pair features: surface points with normals and boundary points with directions. I will describe a max-margin learning framework to identify discriminative features on the surface of the objects. The algorithm selects and ranks features according to their importance for the specified task which leads to improved accuracy and reduced computational cost.
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  •  AWARD   MERL's Speech Team Achieves World's 2nd Best Performance at the Third CHiME Speech Separation and Recognition Challenge
    Date: December 15, 2015
    Awarded to: John R. Hershey, Takaaki Hori, Jonathan Le Roux and Shinji Watanabe
    MERL Contacts: John R. Hershey; Takaaki Hori; Jonathan Le Roux
    Research Areas: Multimedia, Speech & Audio
    Brief
    • The results of the third 'CHiME' Speech Separation and Recognition Challenge were publicly announced on December 15 at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015) held in Scottsdale, Arizona, USA. MERL's Speech and Audio Team, in collaboration with SRI, ranked 2nd out of 26 teams from Europe, Asia and the US. The task this year was to recognize speech recorded using a tablet in real environments such as cafes, buses, or busy streets. Due to the high levels of noise and the distance from the speaker's mouth to the microphones, this is very challenging task, where the baseline system only achieved 33.4% word error rate. The MERL/SRI system featured state-of-the-art techniques including multi-channel front-end, noise-robust feature extraction, and deep learning for speech enhancement, acoustic modeling, and language modeling, leading to a dramatic 73% reduction in word error rate, down to 9.1%. The core of the system has since been released as a new official challenge baseline for the community to use.
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  •  AWARD   2015 IEEE Signal Processing Society Best Paper Award
    Date: December 1, 2015
    Awarded to: Mark A. Davenport, Petros T. Boufounos, Michael B. Wakin and Richard G. Baraniuk
    MERL Contact: Petros T. Boufounos
    Research Areas: Multimedia, Computational Sensing
    Brief
    • Petros Boufounos is a recipient of the 2015 IEEE Signal Processing Society Best Paper Award for the paper that he co-authored with Mark A. Davenport, Michael B. Wakin and Richard G. Baraniuk on "Signal Processing with Compressive Measurements" which was published in the April 2010 issue of IEEE Journal of Selected Topics in Signal Processing. The Best Paper Award honors the author(s) of a paper of exceptional merit dealing with a subject related to the Society's technical scope, and appearing in one of the Society's solely owned transactions or the Journal of Selected Topics in Signal Processing. Eligibility is based on a five-year window: for example, for the 2015 Award, the paper must have appeared in one of the Society's Transactions between January 1, 2010 and December 31, 2014.
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  •  NEWS   MERL presented 3 papers at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
    Date: December 15, 2015
    Where: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
    MERL Contacts: Andrew Knyazev; Hassan Mansour; Dong Tian
    Research Areas: Algorithms, Multimedia, Computer Vision, Machine Learning, Speech & Audio, Electronics & Communications, Signal Processing, Wireless Communications, Digital Video
    Brief
    • MERL researcher Andrew Knyazev gave 3 talks at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). The papers were published in IEEE conference proceedings.
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