Gordon Wichern

Gordon Wichern
Gordon's Quick Links

  • Biography

    Gordon's research interests are at the intersection of signal processing and machine learning applied to speech, music, and environmental sounds. Prior to joining MERL, Gordon worked at iZotope inc. developing audio signal processing software, and at MIT Lincoln Laboratory where he worked in radar target tracking.

  • Recent News & Events

    •  NEWS    MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
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    •  TALK    [MERL Seminar Series 2024] Samuel Clarke presents talk titled Audio for Object and Spatial Awareness
      Date & Time: Wednesday, October 30, 2024; 1:00 PM
      Speaker: Samuel Clarke, Stanford University
      MERL Host: Gordon Wichern
      Research Areas: Artificial Intelligence, Machine Learning, Robotics, Speech & Audio
      Abstract
      • Acoustic perception is invaluable to humans and robots in understanding objects and events in their environments. These sounds are dependent on properties of the source, the environment, and the receiver. Many humans possess remarkable intuition both to infer key properties of each of these three aspects from a sound and to form expectations of how these different aspects would affect the sound they hear. In order to equip robots and AI agents with similar if not stronger capabilities, our research has taken a two-fold path. First, we collect high-fidelity datasets in both controlled and uncontrolled environments which capture real sounds of objects and rooms. Second, we introduce differentiable physics-based models that can estimate acoustic properties of objects and rooms from minimal amounts of real audio data, then can predict new sounds from these objects and rooms under novel, “unseen” conditions.
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    See All News & Events for Gordon

  • Awards

    •  AWARD    MERL team wins the Listener Acoustic Personalisation (LAP) 2024 Challenge
      Date: August 29, 2024
      Awarded to: Yoshiki Masuyama, Gordon Wichern, Francois G. Germain, Christopher Ick, and Jonathan Le Roux
      MERL Contacts: François Germain; Jonathan Le Roux; Gordon Wichern; Yoshiki Masuyama
      Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
      Brief
      • MERL's Speech & Audio team ranked 1st out of 7 teams in Task 2 of the 1st SONICOM Listener Acoustic Personalisation (LAP) Challenge, which focused on "Spatial upsampling for obtaining a high-spatial-resolution HRTF from a very low number of directions". The team was led by Yoshiki Masuyama, and also included Gordon Wichern, Francois Germain, MERL intern Christopher Ick, and Jonathan Le Roux.

        The LAP Challenge workshop and award ceremony was hosted by the 32nd European Signal Processing Conference (EUSIPCO 24) on August 29, 2024 in Lyon, France. Yoshiki Masuyama presented the team's method, "Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization", and received the award from Prof. Michele Geronazzo (University of Padova, IT, and Imperial College London, UK), Chair of the Challenge's Organizing Committee.

        The LAP challenge aims to explore challenges in the field of personalized spatial audio, with the first edition focusing on the spatial upsampling and interpolation of head-related transfer functions (HRTFs). HRTFs with dense spatial grids are required for immersive audio experiences, but their recording is time-consuming. Although HRTF spatial upsampling has recently shown remarkable progress with approaches involving neural fields, HRTF estimation accuracy remains limited when upsampling from only a few measured directions, e.g., 3 or 5 measurements. The MERL team tackled this problem by proposing a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject at the measured directions from a library of subjects. The HRTF of the retrieved subject at the target direction is fed into the neural field in addition to the desired sound source direction. The team also developed a neural network architecture that can handle an arbitrary number of retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate.
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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; Gordon Wichern; Yoshiki Masuyama
      Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
      Brief
      • 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.
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    •  AWARD    MERL Intern and Researchers Win ICASSP 2023 Best Student Paper Award
      Date: June 9, 2023
      Awarded to: Darius Petermann, Gordon Wichern, Aswin Subramanian, Jonathan Le Roux
      MERL Contacts: Jonathan Le Roux; Gordon Wichern
      Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
      Brief
      • Former MERL intern Darius Petermann (Ph.D. Candidate at Indiana University) has received a Best Student Paper Award at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) for the paper "Hyperbolic Audio Source Separation", co-authored with MERL researchers Gordon Wichern and Jonathan Le Roux, and former MERL researcher Aswin Subramanian. The paper presents work performed during Darius's internship at MERL in the summer 2022. The paper introduces a framework for audio source separation using embeddings on a hyperbolic manifold that compactly represent the hierarchical relationship between sound sources and time-frequency features. Additionally, the code associated with the paper is publicly available at https://github.com/merlresearch/hyper-unmix.

        ICASSP is the flagship conference of the IEEE Signal Processing Society (SPS). ICASSP 2023 was held in the Greek island of Rhodes from June 04 to June 10, 2023, and it was the largest ICASSP in history, with more than 4000 participants, over 6128 submitted papers and 2709 accepted papers. Darius’s paper was first recognized as one of the Top 3% of all papers accepted at the conference, before receiving one of only 5 Best Student Paper Awards during the closing ceremony.
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    •  AWARD    Joint CMU-MERL team wins DCASE2023 Challenge on Automated Audio Captioning
      Date: June 1, 2023
      Awarded to: Shih-Lun Wu, Xuankai Chang, Gordon Wichern, Jee-weon Jung, Francois Germain, Jonathan Le Roux, Shinji Watanabe
      MERL Contacts: François Germain; Jonathan Le Roux; Gordon Wichern
      Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
      Brief
      • A joint team consisting of members of CMU Professor and MERL Alumn Shinji Watanabe's WavLab and members of MERL's Speech & Audio team ranked 1st out of 11 teams in the DCASE2023 Challenge's Task 6A "Automated Audio Captioning". The team was led by student Shih-Lun Wu and also featured Ph.D. candidate Xuankai Chang, Postdoctoral research associate Jee-weon Jung, Prof. Shinji Watanabe, and MERL researchers Gordon Wichern, Francois Germain, and Jonathan Le Roux.

        The IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE Challenge), started in 2013, has been organized yearly since 2016, and gathers challenges on multiple tasks related to the detection, analysis, and generation of sound events. This year, the DCASE2023 Challenge received over 428 submissions from 123 teams across seven tasks.

        The CMU-MERL team competed in the Task 6A track, Automated Audio Captioning, which aims at generating informative descriptions for various sounds from nature and/or human activities. The team's system made strong use of large pretrained models, namely a BEATs transformer as part of the audio encoder stack, an Instructor Transformer encoding ground-truth captions to derive an audio-text contrastive loss on the audio encoder, and ChatGPT to produce caption mix-ups (i.e., grammatical and compact combinations of two captions) which, together with the corresponding audio mixtures, increase not only the amount but also the complexity and diversity of the training data. The team's best submission obtained a SPIDEr-FL score of 0.327 on the hidden test set, largely outperforming the 2nd best team's 0.315.
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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 & Audio
      Brief
      • 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.
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  • Research Highlights

  • Internships with Gordon

    • SA0040: Internship - Sound event and anomaly detection

      We are seeking graduate students interested in helping advance the fields of sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds. The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, integrate audio signals with other sensors (electrical, vision, vibration, etc.), and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work.

      The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, physics informed machine learning, outlier detection, and unsupervised learning.

      Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

    See All Internships at MERL

  • MERL Publications

    •  Chakrabarty, A., Deshpande, V.M., Wichern, G., Berntorp, K., "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE Annual Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-180 PDF
      • @inproceedings{Chakrabarty2024dec,
      • author = {Chakrabarty, Ankush and Deshpande, Vedang M. and Wichern, Gordon and Berntorp, Karl}},
      • title = {Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-180}
      • }
    •  Ick, Christopher, Wichern, Gordon, Masuyama, Yoshiki, Germain, François G, Le Roux, Jonathan, "Spatially-Aware Losses for Enhanced Neural Acoustic Fields", Tech. Rep. TR2024-169, Mitsubishi Electric Research Laboratories, Cambridge, MA, December 2024.
      BibTeX TR2024-169 PDF
      • @techreport{MERL_TR2024-169,
      • author = {Ick, Christopher; Wichern, Gordon; Masuyama, Yoshiki; Germain, François G; Le Roux, Jonathan},
      • title = {Spatially-Aware Losses for Enhanced Neural Acoustic Fields},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2024-169},
      • month = dec,
      • year = 2024,
      • url = {https://www.merl.com/publications/TR2024-169/}
      • }
    •  Ick, C., Wichern, G., Masuyama, Y., Germain, F.G., Le Roux, J., "Spatially-Aware Losses for Enhanced Neural Acoustic Fields", NeurIPS 2024 Audio Imagination Workshop, December 2024.
      BibTeX
      • @inproceedings{Ick2024dec,
      • author = {{Ick, Christopher and Wichern, Gordon and Masuyama, Yoshiki and Germain, François G and Le Roux, Jonathan}},
      • title = {Spatially-Aware Losses for Enhanced Neural Acoustic Fields},
      • booktitle = {NeurIPS 2024 Audio Imagination Workshop},
      • year = 2024,
      • month = dec
      • }
    •  Saijo, K., Ebbers, J., Germain, F.G., Wichern, G., Le Roux, J., "Task-Aware Unified Source Separation", arXiv, October 2024.
      BibTeX arXiv
      • @article{Saijo2024oct,
      • author = {Saijo, Kohei and Ebbers, Janek and Germain, François G and Wichern, Gordon and Le Roux, Jonathan}},
      • title = {Task-Aware Unified Source Separation},
      • journal = {arXiv},
      • year = 2024,
      • month = oct,
      • url = {https://arxiv.org/abs/2410.23987v1}
      • }
    •  Saijo, K., Ebbers, J., Germain, F.G., Khurana, S., Wichern, G., Le Roux, J., "Leveraging Audio-Only Data for Text-Queried Target Sound Extraction", arXiv, September 2024.
      BibTeX arXiv
      • @article{Saijo2024sep3,
      • author = {{Saijo, Kohei and Ebbers, Janek and Germain, François G and Khurana, Sameer and Wichern, Gordon and Le Roux, Jonathan}},
      • title = {Leveraging Audio-Only Data for Text-Queried Target Sound Extraction},
      • journal = {arXiv},
      • year = 2024,
      • month = sep,
      • url = {https://arxiv.org/abs/2409.13152v1}
      • }
    See All MERL Publications for Gordon

  • Other Publications

    •  G. Wichern and A. Lukin, "Low-Latency approximation of bidirectional recurrent networks for speech denoising", 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Oct 2017, pp. 66-70.
      BibTeX
      • @Inproceedings{8169996,
      • author = {Wichern, G. and Lukin, A.},
      • title = {Low-Latency approximation of bidirectional recurrent networks for speech denoising},
      • booktitle = {2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
      • year = 2017,
      • pages = {66--70},
      • month = {Oct}
      • }
    •  G. Wichern, H. Robertson and A. Wishnick, "Quantitative Analysis of Masking in Multitrack Mixes Using Loudness Loss", Sep 2016, Audio Engineering Society Convention 141.
      BibTeX External
      • @Conference{wichern2016quantitative,
      • author = {Wichern, G. and Robertson, H. and Wishnick, A.},
      • title = {Quantitative Analysis of Masking in Multitrack Mixes Using Loudness Loss},
      • booktitle = {Audio Engineering Society Convention 141},
      • year = 2016,
      • month = {Sep},
      • url = {http://www.aes.org/e-lib/browse.cfm?elib=18450}
      • }
    •  G. Wichern, A. Wishnick, A. Lukin and H. Robertson, "Comparison of Loudness Features for Automatic Level Adjustment in Mixing", Oct 2015, Audio Engineering Society Convention 139.
      BibTeX External
      • @Conference{wichern2015comparison,
      • author = {Wichern, G. and Wishnick, A. and Lukin, A. and Robertson, H.},
      • title = {Comparison of Loudness Features for Automatic Level Adjustment in Mixing},
      • booktitle = {Audio Engineering Society Convention 139},
      • year = 2015,
      • month = {Oct},
      • url = {http://www.aes.org/e-lib/browse.cfm?elib=17928}
      • }
    •  M. Yamada, G. Wichern, K. Kondo, M. Sugiyama and H. Sawada, "Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis", Digital Signal Processing, Vol. 23, No. 1, pp. 1-8, 2013.
      BibTeX
      • @Article{yamada2013noise,
      • author = {Yamada, M. and Wichern, G. and Kondo, K. and Sugiyama, M. and Sawada, H.},
      • title = {Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis},
      • journal = {Digital Signal Processing},
      • year = 2013,
      • volume = 23,
      • number = 1,
      • pages = {1--8},
      • publisher = {Academic Press}
      • }
    •  M. Yamada, M. Sugiyama, G. Wichern and J. Simm, "Improving the accuracy of least-squares probabilistic classifiers", IEICE transactions on information and systems, Vol. 94, No. 6, pp. 1337-1340, 2011.
      BibTeX
      • @Article{yamada2011improving,
      • author = {Yamada, M. and Sugiyama, M. and Wichern, G. and Simm, J.},
      • title = {Improving the accuracy of least-squares probabilistic classifiers},
      • journal = {IEICE transactions on information and systems},
      • year = 2011,
      • volume = 94,
      • number = 6,
      • pages = {1337--1340},
      • publisher = {The Institute of Electronics, Information and Communication Engineers}
      • }
    •  M. Shah, G. Wichern, A. Spanias and H. Thornburg, "Audio content-based feature extraction algorithms using J-DSP for arts, media and engineering courses", 2010 IEEE Frontiers in Education Conference (FIE), Oct 2010, pp. T1F-1-T1F-6.
      BibTeX
      • @Inproceedings{5673157,
      • author = {Shah, M. and Wichern, G. and Spanias, A. and Thornburg, H.},
      • title = {Audio content-based feature extraction algorithms using J-DSP for arts, media and engineering courses},
      • booktitle = {2010 IEEE Frontiers in Education Conference (FIE)},
      • year = 2010,
      • pages = {T1F--1--T1F--6},
      • month = {Oct}
      • }
    •  G. Wichern, J. Xue, H. Thornburg, B. Mechtley and A. Spanias, "Segmentation, Indexing, and Retrieval for Environmental and Natural Sounds", IEEE Transactions on Audio, Speech, and Language Processing, Vol. 18, No. 3, pp. 688-707, March 2010.
      BibTeX
      • @Article{5410056,
      • author = {Wichern, G. and Xue, J. and Thornburg, H. and Mechtley, B. and Spanias, A.},
      • title = {Segmentation, Indexing, and Retrieval for Environmental and Natural Sounds},
      • journal = {IEEE Transactions on Audio, Speech, and Language Processing},
      • year = 2010,
      • volume = 18,
      • number = 3,
      • pages = {688--707},
      • month = mar
      • }
    •  M. Yamada, M. Sugiyama and G. Wichern, "Direct importance estimation with probabilistic principal component analyzers", 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2010, pp. 1962-1965.
      BibTeX
      • @Inproceedings{5495290,
      • author = {Yamada, M. and Sugiyama, M. and Wichern, G.},
      • title = {Direct importance estimation with probabilistic principal component analyzers},
      • booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
      • year = 2010,
      • pages = {1962--1965},
      • month = mar
      • }
    •  M. Yamada, M. Sugiyama, G. Wichern and T. Matsui, "Acceleration of sequence kernel computation for real-time speaker identification", 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2010, pp. 1626-1629.
      BibTeX
      • @Inproceedings{5495542,
      • author = {Yamada, M. and Sugiyama, M. and Wichern, G. and Matsui, T.},
      • title = {Acceleration of sequence kernel computation for real-time speaker identification},
      • booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
      • year = 2010,
      • pages = {1626--1629},
      • month = mar
      • }
    •  G. Wichern, M. Yamada, H. Thornburg, M. Sugiyama and A. Spanias, "Automatic audio tagging using covariate shift adaptation", 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2010, pp. 253-256.
      BibTeX
      • @Inproceedings{5495973,
      • author = {Wichern, G. and Yamada, M. and Thornburg, H. and Sugiyama, M. and Spanias, A.},
      • title = {Automatic audio tagging using covariate shift adaptation},
      • booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
      • year = 2010,
      • pages = {253--256},
      • month = mar
      • }
    •  B. Mechtley, G. Wichern, H. Thornburg and A. Spanias, "Combining semantic, social, and acoustic similarity for retrieval of environmental sounds", 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2010, pp. 2402-2405.
      BibTeX
      • @Inproceedings{5496225,
      • author = {Mechtley, B. and Wichern, G. and Thornburg, H. and Spanias, A.},
      • title = {Combining semantic, social, and acoustic similarity for retrieval of environmental sounds},
      • booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
      • year = 2010,
      • pages = {2402--2405},
      • month = mar
      • }
    •  A. Fink, B. Mechtley, G. Wichern, J. Liu, H. Thornburg, A. Spanias and G. Coleman, "Re-Sonification of Geographic Sound Activity using Acoustic, Semantic and Social Information", Proceedings of the 16th International Conference on Auditory Display (ICAD2010), 2010.
      BibTeX
      • @Inproceedings{fink2010re,
      • author = {Fink, A. and Mechtley, B. and Wichern, G. and Liu, J. and Thornburg, H. and Spanias, A. and Coleman, G.},
      • title = {Re-Sonification of Geographic Sound Activity using Acoustic, Semantic and Social Information},
      • booktitle = {Proceedings of the 16th International Conference on Auditory Display (ICAD2010)},
      • year = 2010,
      • organization = {Georgia Institute of Technology}
      • }
    •  G. Wichern, B. Mechtley, A. Fink, H. Thornburg and A. Spanias, "An ontological framework for retrieving environmental sounds using semantics and acoustic content", EURASIP Journal on Audio, Speech, and Music Processing, Vol. 2010, No. 1, pp. 192363, 2010.
      BibTeX
      • @Article{wichern2010ontological,
      • author = {Wichern, G. and Mechtley, B. and Fink, A. and Thornburg, H. and Spanias, A.},
      • title = {An ontological framework for retrieving environmental sounds using semantics and acoustic content},
      • journal = {EURASIP Journal on Audio, Speech, and Music Processing},
      • year = 2010,
      • volume = 2010,
      • number = 1,
      • pages = 192363,
      • publisher = {Springer International Publishing}
      • }
    •  M. Yamada, M. Sugiyama, G. Wichern and J. Simm, "Direct importance estimation with a mixture of probabilistic principal component analyzers", IEICE Transactions on Information and Systems, Vol. 93, No. 10, pp. 2846-2849, 2010.
      BibTeX
      • @Article{yamada2010direct,
      • author = {Yamada, M. and Sugiyama, M. and Wichern, G. and Simm, J.},
      • title = {Direct importance estimation with a mixture of probabilistic principal component analyzers},
      • journal = {IEICE Transactions on Information and Systems},
      • year = 2010,
      • volume = 93,
      • number = 10,
      • pages = {2846--2849},
      • publisher = {The Institute of Electronics, Information and Communication Engineers}
      • }
    •  G. Wichern, H. Thornburg and A. Spanias, "Unifying semantic and content-based approaches for retrieval of environmental sounds", 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Oct 2009, pp. 13-16.
      BibTeX
      • @Inproceedings{5346493,
      • author = {Wichern, G. and Thornburg, H. and Spanias, A.},
      • title = {Unifying semantic and content-based approaches for retrieval of environmental sounds},
      • booktitle = {2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
      • year = 2009,
      • pages = {13--16},
      • month = {Oct}
      • }
    •  G. Wichern, H. Kwon, A. Spanias, A. Fink and H. Thornburg, "Continuous observation and archival of acoustic scenes using wireless sensor networks", 2009 16th International Conference on Digital Signal Processing, July 2009, pp. 1-6.
      BibTeX
      • @Inproceedings{5201082,
      • author = {Wichern, G. and Kwon, H. and Spanias, A. and Fink, A. and Thornburg, H.},
      • title = {Continuous observation and archival of acoustic scenes using wireless sensor networks},
      • booktitle = {2009 16th International Conference on Digital Signal Processing},
      • year = 2009,
      • pages = {1--6},
      • month = jul
      • }
    •  G. Wichern, H. Thornburg and A. Spanias, "Multi-channel audio segmentation for continuous observation and archival of large spaces", 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, April 2009, pp. 237-240.
      BibTeX
      • @Inproceedings{4959564,
      • author = {Wichern, G. and Thornburg, H. and Spanias, A.},
      • title = {Multi-channel audio segmentation for continuous observation and archival of large spaces},
      • booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
      • year = 2009,
      • pages = {237--240},
      • month = apr
      • }
    •  J. Xue, G. Wichern, H. Thornburg and A. Spanias, "Fast query by example of environmental sounds via robust and efficient cluster-based indexing", 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2008, pp. 5-8.
      BibTeX
      • @Inproceedings{4517532,
      • author = {Xue, J. and Wichern, G. and Thornburg, H. and Spanias, A.},
      • title = {Fast query by example of environmental sounds via robust and efficient cluster-based indexing},
      • booktitle = {2008 IEEE International Conference on Acoustics, Speech and Signal Processing},
      • year = 2008,
      • pages = {5--8},
      • month = mar
      • }
    •  G. Wichern, J. Xue, H. Thornburg and A. Spanias, "Distortion-Aware Query-by-Example for Environmental Sounds", 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Oct 2007, pp. 335-338.
      BibTeX
      • @Inproceedings{4393051,
      • author = {Wichern, G. and Xue, J. and Thornburg, H. and Spanias, A.},
      • title = {Distortion-Aware Query-by-Example for Environmental Sounds},
      • booktitle = {2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
      • year = 2007,
      • pages = {335--338},
      • month = {Oct}
      • }
    •  M. McCarron, M. R. Azimi-Sadjadi, G. Wichem and M. Mungiole, "An Operationally Adaptive System for Rapid Acoustic Transmission Loss Prediction", 2007 International Joint Conference on Neural Networks, Aug 2007, pp. 2262-2267.
      BibTeX
      • @Inproceedings{4371310,
      • author = {McCarron, M. and Azimi-Sadjadi, M. R. and Wichem, G. and Mungiole, M.},
      • title = {An Operationally Adaptive System for Rapid Acoustic Transmission Loss Prediction},
      • booktitle = {2007 International Joint Conference on Neural Networks},
      • year = 2007,
      • pages = {2262--2267},
      • month = {Aug}
      • }
    •  G. Wichern, H. Thornburg, B. Mechtley, A. Fink, K. Tu and A. Spanias, "Robust Multi-Features Segmentation and Indexing for Natural Sound Environments", 2007 International Workshop on Content-Based Multimedia Indexing, June 2007, pp. 69-76.
      BibTeX
      • @Inproceedings{4275057,
      • author = {Wichern, G. and Thornburg, H. and Mechtley, B. and Fink, A. and Tu, K. and Spanias, A.},
      • title = {Robust Multi-Features Segmentation and Indexing for Natural Sound Environments},
      • booktitle = {2007 International Workshop on Content-Based Multimedia Indexing},
      • year = 2007,
      • pages = {69--76},
      • month = jun
      • }
    •  G. Wichern, M. R. Azimi-Sadjadi and M. Mungiole, "Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres", Neural Networks, Vol. 20, No. 4, pp. 484 - 497, 2007.
      BibTeX External
      • @Article{WICHERN2007484,
      • author = {Wichern, G. and Azimi-Sadjadi, M. R. and Mungiole, M.},
      • title = {Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres},
      • journal = {Neural Networks},
      • year = 2007,
      • volume = 20,
      • number = 4,
      • pages = {484 -- 497},
      • note = {Computational Intelligence in Earth and Environmental Sciences},
      • url = {http://www.sciencedirect.com/science/article/pii/S089360800700055X}
      • }
    •  G. Wichern, M. R. Azimi-Sadjadi and M. Mungiole, "An Environmentally Adaptive System for Rapid Acoustic Transmission Loss Prediction", The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006, pp. 5118-5125.
      BibTeX
      • @Inproceedings{1716812,
      • author = {Wichern, G. and Azimi-Sadjadi, M. R. and Mungiole, M.},
      • title = {An Environmentally Adaptive System for Rapid Acoustic Transmission Loss Prediction},
      • booktitle = {The 2006 IEEE International Joint Conference on Neural Network Proceedings},
      • year = 2006,
      • pages = {5118--5125}
      • }
    •  MR Azimi-Sadjadi, Y Jiang and G Wichern, "Properties of randomly distributed sparse arrays", Proc. SPIE, 2006, vol. 6201.
      BibTeX
      • @Inproceedings{azimi2006properties,
      • author = {Azimi-Sadjadi, MR and Jiang, Y and Wichern, G},
      • title = {Properties of randomly distributed sparse arrays},
      • booktitle = {Proc. SPIE},
      • year = 2006,
      • volume = 6201
      • }
    •  MR Azimi-Sadjadi, A Pezeshki, LL Scharf and G Wichern, "Unattended sparse acoustic array configurations and beamforming algorithms", Proc. SPIE, 2005, vol. 5796, pp. 40-51.
      BibTeX
      • @Inproceedings{azimi2005unattended,
      • author = {Azimi-Sadjadi, MR and Pezeshki, A and Scharf, LL and Wichern, G},
      • title = {Unattended sparse acoustic array configurations and beamforming algorithms},
      • booktitle = {Proc. SPIE},
      • year = 2005,
      • volume = 5796,
      • pages = {40--51}
      • }

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  • MERL Issued Patents

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