Josh McDermott

Photo of Prof. Josh McDermott
Josh
McDermott
Investigator
Massachusetts Institute of Technology (MIT)
Department:  Department of Brain and Cognitive Sciences

Associated Research Module: 

Associated Research Thrust: 

Josh McDermott obtained his PhD from MIT in 2006 and returned in January 2013 as an Assistant Professor in the Department of Brain and Cognitive Sciences, moving from Oxford University, where he was a visiting scientist during 2012. Prior to that, he was a research associate at New York University (2009-2012) and a postdoctoral fellow at the University of Minnesota (2007-2008). Dr. McDermott is the recipient of a Marshall Scholarship, a James S. McDonnell Foundation Scholar Award, and an NSF CAREER Award.

Dr. McDermott studies sound and hearing using tools from experimental psychology, engineering, and neuroscience. He seeks to understand how humans derive information from sound, and in particular how they succeed in real-world conditions that cause even the most powerful state-of-the-art computer algorithms to fail, for instance in recognizing speech amid background noise. He aims to use the contrast between biological and machine hearing systems to reveal the workings of biological hearing, to improve prosthetic devices for aiding those with hearing impairment, and to design better computer algorithms for analyzing sound. Research in his lab will explore how humans recognize real-world sound sources, segregate particular sounds from the mixture that enters the ear (the cocktail party problem), and remember and/or attend to particular sounds of interest. He also studies music perception and cognition.

Room:  MIT Bldg. 46, Rm. 46-4065
Phone:  (617) 253-7437
Website: 
Lab Website:  Laboratory for Computational Audition , McDermott Lab

Current Advisees

Maddie Cusimano - Graduate Student
Alex Durango - Graduate Student
Jenelle Feather - Graduate Student
Andrew Francl - Graduate Student
Jarrod Hicks - Graduate Student
Mark Saddler - Graduate Student

Past Advisees

Alex Kell - Graduate Student
Richard McWalter - Postdoc
Wiktor Młynarski - Postdoctoral Associate
Erica Shook - Visiting Research Intern
James Traer - Graduate Student

Projects

Lossy Compression in Auditory Perception
Mid-Level Representations of Natural Sounds

CBMM Publications

A. Francl and McDermott, J. H., “Deep neural network models of sound localization reveal how perception is adapted to real-world environments”, Nature Human Behavior, vol. 6, pp. 111–133 , 2022.
M. J. McPherson, Grace, R. C., and McDermott, J. H., “Harmonicity aids hearing in noise”, Attention, Perception, & Psychophysics, 2022.
S. V. Norman-Haignere, Feather, J., Boebinger, D., Brunner, P., Ritaccio, A., McDermott, J. H., Schalk, G., and Kanwisher, N., “A neural population selective for song in human auditory cortex”, Current Biology, 2022.
J. Traer, Norman-Haignere, S., and McDermott, J. H., “Causal inference in environmental sound recognition”, Cognition, 2021.
M. R. Saddler, Gonzalez, R., and McDermott, J. H., “Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception”, Nature Communications, vol. 12, no. 1, 2021.
J. M. Hicks and McDermott, J. H., “Segregation from Noise as Outlier Detection ”, in Association for Research in Otolaryngology, San Jose, CA, USA, 2020.
C. Gen, Schwartz, J., Alter, S., Schrimpf, M., Traer, J., De Freitas, J., Kubilius, J., Bhandwaldar, A., Haber, N., Sano, M., Kim, K., Wang, E., Mrowca, D., Lingelbach, M., Curtis, A., Feigleis, K., Bear, D., Gutfreund, D., Cox, D., DiCarlo, J. J., McDermott, J. H., Tenenbaum, J. B., and Yamins, D. L. K., “ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation”, arXiv, 2020.
J. Schwartz, Alter, S., DiCarlo, J. J., McDermott, J. H., Tenenbaum, J. B., Yamins, D. L. K., Gutfreund, D., Gan, C., Traer, J., Kubilius, J., Schrimpf, M., Bhandwaldar, A., De Freitas, J., Mrowca, D., Lingelbach, M., Sano, M., Bear, D., Kim, K., Haber, N., and Fan, C., “ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation”. 2020.
M. J. McPherson and McDermott, J. H., “Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory”, Proceedings of the National Academy of Sciences, vol. 117, no. 50, pp. 32169 - 32180, 2020.
A. J. E. Kell and McDermott, J. H., “Deep neural network models of sensory systems: windows onto the role of task constraints”, Current Opinion in Neurobiology, vol. 55, pp. 121 - 132, 2019.
S. V. Norman-Haignere, Kanwisher, N., McDermott, J. H., and Conway, B. R., “Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones”, Nature Neuroscience, 2019.
M. Cusimano, Traer, J., and McDermott, J. H., “Scrape, rub, and roll: causal inference in the perception of sustained contact sounds ”, in Cognitive Science, Montreal, Québec, Canada, 2019.
A. J. E. Kell and McDermott, J. H., “Invariance to background noise as a signature of non-primary auditory cortex”, Nature Communications, vol. 10, no. 1, 2019.
J. Traer, Cusimano, M., and McDermott, J. H., “A perceptually inspired generative model of rigid-body contact sounds”, Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), 2019.
N. Jacoby, Undurraga, E. A., McPherson, M. J., Valdés, J., Ossandón, T., and McDermott, J. H., “Universal and Non-universal Features of Musical Pitch Perception Revealed by Singing”, Current Biology, 2019.
C. Stephenson, Feather, J., Padhy, S., Elibol, O., Tang, H., McDermott, J. H., and Chung, S. Y., “Untangling in Invariant Speech Recognition”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.
J. Feather, Durango, A., Gonzalez, R., and McDermott, J. H., “Metamers of neural networks reveal divergence from human perceptual systems”, NIPS 2019. Vancouver, Canada, 2019.
R. McWalter and McDermott, J. H., “Illusory sound texture reveals multi-second statistical completion in auditory scene analysis”, Nature Communications, vol. 10, no. 1, 2019.
W. Młynarski and McDermott, J. H., “Ecological origins of perceptual grouping principles in the auditory system”, Proceedings of the National Academy of Sciences, vol. 116, no. 50, pp. 25355 - 25364, 2019.
W. Mlynarski and McDermott, J. H., “Co-occurrence statistics of natural sound features predict perceptual grouping”, Computational and Systems Neuroscience (Cosyne) 2018. 2018.
J. Traer and McDermott, J. H., “Human recognition of environmental sounds is not always robust to reverberation”, Annual Meeting of the Acoustical Society, vol. 143, no. 3. 2018.
W. Mlynarski and McDermott, J. H., “Co-occurrence statistics of natural sound features predict perceptual grouping”, Computational and Systems Neuroscience (COSYNE). Denver, Colorado, 2018.
W. Mlynarski and McDermott, J. H., “Learning Mid-Level Auditory Codes from Natural Sound Statistics”, Neural Computation, vol. 30, no. 3, pp. 631-669, 2018.
J. Traer and McDermott, J. H., “Human inference of force from impact sounds: Perceptual evidence for inverse physics”, Annual Meeting of the Acoustical Society, vol. 143, no. 3. 2018.
A. J. E. Kell, Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., and McDermott, J. H., “A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy”, Neuron, vol. 98, 2018.
J. Traer and McDermott, J. H., “Auditory Perception of Material and Force from Impact Sounds”, Annual Meeting of Association for Research in Otolaryngology. 2017.
W. Mlynarski and McDermott, J. H., “Adaptive Compression of Statistically Homogenous Sensory Signals”, Computational and Systems Neuroscience (COSYNE). 2017.
J. Traer and McDermott, J. H., “Investigating audition with a generative model of impact sounds”, Annual Meeting of Acoustical Society of America. 2017.
W. Mlynarski and McDermott, J. H., “Lossy Compression of Uninformative Stimuli in the Auditory System”, Association for Otolaryngology Mid-Winter Meeting. 2017.
J. Traer and McDermott, J. H., “A library of real-world reverberation and a toolbox for its analysis and measurement”, Annual Meeting of Acoustical Society of America. 2017.
W. Mlynarski and McDermott, J. H., “Learning Mid-Level Codes for Natural Sounds”, Association for Otolaryngology Mid-Winter Meeting. 2017.
W. Mlynarski and McDermott, J. H., “Learning Mid-Level Auditory Codes from Natural Sound Statistics”. 2017.
Z. Zhang, Wu, J., Li, Q., Huang, Z., Traer, J., McDermott, J. H., Tenenbaum, J. B., and Freeman, W. T., “Generative modeling of audible shapes for object perception”, in The IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017.
W. Mlynarski and McDermott, J. H., “Lossy Compression of Sound Texture by the Human Auditory System”, Society for Neuroscience Meeting. 2016.
J. Traer and McDermott, J. H., “Environmental statistics enable perceptual separation of sound and space”, Speech and Audio in the Northeast. 2016.
W. Mlynarski and McDermott, J. H., “Learning Mid-Level Codes for Natural Sounds”, Advances and Perspectives in Auditory Neuroscience. 2016.
W. Mlynarski and McDermott, J. H., “Learning mid-level codes for natural sounds”, Computational and Systems Neuroscience (Cosyne) 2016. Salt Lake City, UT, 2016.
A. Owens, Isola, P., McDermott, J. H., Torralba, A., Adelson, E. H., and Freeman, W. T., “Visually indicated sounds”, in Conference on Computer Vision and Pattern Recognition, 2016.
J. Traer and McDermott, J. H., “Statistics of natural reverberation enable perceptual separation of sound and space”, Proceedings of the National Academy of Sciences, vol. 113, no. 48, pp. E7856 - E7865, 2016.
A. Owens, Isola, P., McDermott, J. H., Freeman, W. T., and Torralba, A., “Lecture Notes in Computer ScienceComputer Vision – ECCV 2016Ambient Sound Provides Supervision for Visual Learning”, 14th European Conference on Computer Vision. Cham, pp. 801 - 816, 2016.