Grants per year
Personal profile
Research Interests
Professor Pardo develops new methods in Machine Learning, Signal Processing and Human Computer Interaction to make new tools for understanding and manipulating sound. Ongoing research in his lab is applied to audio scene labeling, audio source separation, inclusive interfaces, new audio production tools and machine audition models that learn without supervision.
Education/Academic qualification
Computer Science and Engineering, PhD, University of Michigan
… → 2005
Jazz and Improvisation, MMus, University of Michigan
… → 2001
Computer Science, MS, Ohio State University
… → 1993
Jazz Composition, BMus, Ohio State University
… → 1990
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Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Collaborative Research: FW-HTF-R: Toward an Ecosystem of Artificial-intelligence-powered Music Production (TEAMuP)
10/1/22 → 9/30/26
Project: Research project
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CHS: Medium: Next Generation Content Production Tools for People with Vision Impairments
Gergle, D., Piper, A. M., Gergle, D. & Pardo, B. A.
10/1/19 → 9/30/23
Project: Research project
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REU Supplement III: Small: Collaborative Research: Algorithms for Query by Example of Audio Databases
9/1/16 → 8/31/20
Project: Research project
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III: Small: Collaborative Research: Algorithms for Query by Example of Audio Databases
9/1/16 → 8/31/20
Project: Research project
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Participant Support Subproject for CHS:Small: Robust Interactive Audio Source Separation
10/1/14 → 9/30/18
Project: Research project
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EFFECTIVE AND INCONSPICUOUS OVER-THE-AIR ADVERSARIAL EXAMPLES WITH ADAPTIVE FILTERING
O'Reilly, P., Awasthi, P., Vijayaraghavan, A. & Pardo, B., 2022, 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 6607-6611 5 p. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2022-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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IMPROVING SOURCE SEPARATION BY EXPLICITLY MODELING DEPENDENCIES BETWEEN SOURCES
Manilow, E., Hawthorne, C., Huang, C. Z. A., Pardo, B. & Engel, J., 2022, 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 291-295 5 p. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2022-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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SOURCE SEPARATION BY STEERING PRETRAINED MUSIC MODELS
Manilow, E., O'Reilly, P., Seetharaman, P. & Pardo, B., 2022, 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 126-130 5 p. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2022-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Context-Aware Prosody Correction for Text-Based Speech Editing
Morrison, M., Rencker, L., Jin, Z., Bryan, N. J., Caceres, J. P. & Pardo, B., 2021, In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2021-June, p. 7038-7042 5 p.Research output: Contribution to journal › Conference article › peer-review
3 Scopus citations -
Autoclip: Adaptive gradient clipping for source separation networks
Seetharaman, P., Wichern, G., Pardo, B. & Roux, J. L., Sep 2020, Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE Computer Society, 9231926. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; vol. 2020-September).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
7 Scopus citations