Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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CBMM Memos

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Auditory Codes from Natural Sound Statistics. (2017).

MlynarskiMcDermott_Memo060.pdf (7.11 MB)

Code

Schwartz, J. et al. ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <http://www.threedworld.org/>

Conference Abstract

Traer, J. & McDermott, J. H. Auditory Perception of Material and Force from Impact Sounds. Annual Meeting of Association for Research in Otolaryngology (2017).

Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (Cosyne) 2018 (2018).

Traer, J. & McDermott, J. H. Environmental statistics enable perceptual separation of sound and space. Speech and Audio in the Northeast (2016).

Traer, J. & McDermott, J. H. Human inference of force from impact sounds: Perceptual evidence for inverse physics. Annual Meeting of the Acoustical Society 143, (2018).

Traer, J. & McDermott, J. H. Human recognition of environmental sounds is not always robust to reverberation. Annual Meeting of the Acoustical Society 143, (2018).

Traer, J. & McDermott, J. H. Investigating audition with a generative model of impact sounds. Annual Meeting of Acoustical Society of America (2017).

Mlynarski, W. & McDermott, J. H. Learning mid-level codes for natural sounds. Computational and Systems Neuroscience (Cosyne) 2016 (2016). at <http://www.cosyne.org/c/index.php?title=Cosyne2016_posters_2>

Wiktor_COSYNE_2015_hierarchy_final.pdf (2.52 MB)

Traer, J. & 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).

Conference Paper

Zhang, Z. et al. Generative modeling of audible shapes for object perception. The IEEE International Conference on Computer Vision (ICCV) (2017). at <http://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Generative_Modeling_of_ICCV_2017_paper.html>

Cusimano, M., Traer, J. & McDermott, J. H. Scrape, rub, and roll: causal inference in the perception of sustained contact sounds . Cognitive Science (2019).

Hicks, J. M. & McDermott, J. H. Segregation from Noise as Outlier Detection . Association for Research in Otolaryngology (2020).

Owens, A. et al. Visually indicated sounds. Conference on Computer Vision and Pattern Recognition (2016).

Owens_etal_2016_visually_indicated_sounds_CVPR.pdf (7.57 MB)

Conference Poster

Mlynarski, W. & McDermott, J. H. Adaptive Compression of Statistically Homogenous Sensory Signals. Computational and Systems Neuroscience (COSYNE) (2017).

Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (COSYNE) (2018). at <http://www.cosyne.org/c/index.php?title=Cosyne_18>

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Codes for Natural Sounds. Advances and Perspectives in Auditory Neuroscience (2016).

APAN_large_JHM kopia.pdf (19.74 MB)

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Codes for Natural Sounds. Association for Otolaryngology Mid-Winter Meeting (2017).

Mlynarski, W. & McDermott, J. H. Lossy Compression of Sound Texture by the Human Auditory System. Society for Neuroscience Meeting (2016).

Mlynarski, W. & McDermott, J. H. Lossy Compression of Uninformative Stimuli in the Auditory System. Association for Otolaryngology Mid-Winter Meeting (2017).

Conference Proceedings

Owens, A., Isola, P., McDermott, J. H., Freeman, W. T. & Torralba, A. Lecture Notes in Computer ScienceComputer Vision – ECCV 2016Ambient Sound Provides Supervision for Visual Learning. 14th European Conference on Computer Vision 801 - 816 (2016). doi:10.1007/978-3-319-46448-010.1007/978-3-319-46448-0_48

Feather, J., Durango, A., Gonzalez, R. & McDermott, J. H. Metamers of neural networks reveal divergence from human perceptual systems. NIPS 2019 (2019). at <https://papers.nips.cc/paper/9198-metamers-of-neural-networks-reveal-divergence-from-human-perceptual-systems>

Feather_etal_2019_NeurIPS_metamers.pdf (4.7 MB)

Stephenson, C. et al. Untangling in Invariant Speech Recognition. Neural Information Processing Systems (NeurIPS 2019) (2019).

9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)

Journal Article

Traer, J., Norman-Haignere, S. & McDermott, J. H. Causal inference in environmental sound recognition. Cognition (2021). doi:10.1016/j.cognition.2021.104627

Kell, A. J. E. & McDermott, J. H. Deep neural network models of sensory systems: windows onto the role of task constraints. Current Opinion in Neurobiology 55, 121 - 132 (2019).

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