Publications
2024
- J. Barnett, H. Flores-Garcia, and B. Pardo, “Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model,” in ISMIR, 2024.
- M. Morrison, P. Pawar, N. Pruyne, J. Cole, and B. Pardo, “Crowdsourced and Automatic Speech Prominence Estimation,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
- P. O’Reilly, Z. Jin, J. Su, and B. Pardo, “MaskMark: Robust Neural Watermarking for Real and Synthetic Speech,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
- C. Churchwell, M. Morrison, and B. Pardo, “High Fidelity Neural Phonetic Posteriorgrams,” in ICASSP 2024 Workshop on Explainable AI for Speech and Audio, 2024.
- M. Morrison, C. Churchwell, N. Pruyne, and B. Pardo, “Fine-Grained and Interpretable Neural Speech Editing,” in Interspeech 2024, 2024.
- M. Morrison, “Interpretable Speech Representation and Editing,” PhD thesis, Northwestern University, 2024.
2023
- H. Flores Garcia, P. Seetharaman, R. Kumar, and B. Pardo, “VampNet: Music Generation via Masked Acoustic Token Modeling,” in ISMIR, 2023.
- H. Flores Garcia, P. O’Reilly, A. Aguilar, B. Pardo, C. Benetatos, and Z. Duan, “HARP: Bringing Deep Learning to the DAW with Hosted, Asynchronous, Remote Processing,” in NeurIPS Workshop on Machine Learning for Creativity and Design, 2023.
2022
- P. O’Reilly, A. Bugler, K. Bhandari, M. Morrison, and B. Pardo, “VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models,” in Neural Information Processing Systems, 2022.
- E. Manilow, C. Hawthorne, A. Huang, B. and Pardo, and J. Engel, “Improving Source Separation by Explicitly Modeling Dependencies Between Sources,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.
- E. Manilow, P. O’Reilly, P. Seetharaman, and B. Pardo, “Source Separation by Steering Pretrained Music Models,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.
- M. Morrison, B. Tang, G. Tan, and B. Pardo, “Reproducible Subjective Evaluation,” in ICLR Workshop on Machine Learning Evaluation Standards, 2022.
- E. Rumbold, “A Critical Analysis of Objective Evaluation Metrics for Music Source Separation Quality.” Northwestern University, Evanston, IL, USA, Aug-2022.
- E. Manilow, “Score-Informed and Hierarchical Methods for Computational Musical Scene Analysis,” PhD thesis, Northwestern University, Evanston, IL, USA, 2022.
- N. Schaffer, B. Cogan, E. Manilow, M. Morrison, P. Seetharaman, and B. Pardo, “Music Separation Enhancement with Generative Modeling,” in ISMIR, 2022.
- P. O’Reilly, P. Awasthi, A. Vijayaraghavan, and B. Pardo, “Effective and Inconspicuous Over-the-air Adversarial Examples with Adaptive Filtering,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.
2021
- M. Morrison, L. Rencker, Z. Jin, N. J. Bryan, J.-P. Caceres, and B. Pardo, “Context-Aware Prosody Correction for Text-Based Speech Editing,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
- H. Flores Garcia, A. Aguilar, E. Manilow, and B. Pardo, “Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition,” in Proceedings of the 22nd International Society of Music Information Retrieval Conference (ISMIR 2021), 2021.
- H. Flores Garcia, A. Aguilar, E. Manilow, D. Vedenko, and B. Pardo, “Deep Learning Tools for Audacity: Helping Researchers Expand the Artist’s Toolkit,” in 5th Workshop on Machine Learning for Creativity and Design at NeurIPS 2021, 2021.
2020
- F. Pishdadian, “Auditory-inspired Approaches to Audio Representation and Analysis for Machine Hearing,” PhD thesis, Northwestern University, Evanston, IL, USA, 2020.
- Y. Zhang, J. Hu, Y. Zhang, B. Pardo, and Z. Duan, “Vroom! A Search Engine for Sounds by Vocal Imitation Queries,” in Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, 2020.
- V. Tang, P. Seetharaman, K. Chao, B. A. Pardo, and S. van der Lee, “Automating the Detection of Dynamically Triggered Earthquakes via a Deep Metric Learning Algorithm,” Seismological Research Letters, 2020.
- A. Liu, A. Fang, G. Hadjeres, P. Seetharaman, and B. Pardo, “Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation,” in Machine Learning for Media Discovery Workshop at the 37th International Conference on Machine Learning (ICML), 2020.
- A. Fang, A. Liu, P. Seetharaman, and B. Pardo, “Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach,” in Machine Learning for Media Discovery Workshop at the 37th International Conference on Machine Learning (ICML), 2020.
- P. Seetharaman, G. Wichern, J. LeRoux, and B. Pardo, “Bootstrapping Unsupervised Deep Music Separation from Primitive Auditory Grouping Principles,” in Workshop on Self-supervision in Audio and Speech at the 37th International Conference on Machine Learning(ICML), 2020.
- P. Seetharaman, G. Wichern, B. Pardo, and J. Le Roux, “Autoclip: Adaptive gradient clipping for source separation networks,” in 2020 IEEE International Workshop on Machine Learning for Signal Processing, 2020.
- B. Kim, “Sound Event Annotation and Detection with Less Human Effort,” PhD thesis, Northwestern University, Evanston, IL, USA, 2020.
- A. Liu, P. Seetharaman, and B. Pardo, “Model selection for deep audio source separation via clustering analysis,” Proceedings of the 2020 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2020), 2020.
- E. Manilow, P. Seetharaman, and B. Pardo, “Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.
2019
- P. Seetharaman, “Bootstrapping the Learning Process for Computer Audition,” PhD thesis, Northwestern Uniersity, Evanston, IL, USA, 2019.
- B. Pardo, M. Cartwright, P. Seetharaman, and B. Kim, “Learning to Build Natural Audio Production Interfaces,” Arts, vol. 8, no. 3, 2019.
- P. Seetharaman, G. Mysore, B. Pardo, P. Smaragdis, and C. Gomes, “VoiceAssist: Guiding Users to High-Quality Voice Recordings,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, p. 309.
- B. Kim and B. Pardo, “Improving Content-based Audio Retrieval by Vocal Imitation Feedback,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 4100–4104.
- B. Kim and B. Pardo, “Sound Event Detection using point-labeled data,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2019), New Paltz, NY, USA, 2019.
- P. Seetharaman, G. Wichern, S. Venkataramani, and J. Le Roux, “Class-conditional embeddings for music source separation,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 301–305.
- G. Wichern et al., “WHAM!: Extending Speech Separation to Noisy Environments,” arXiv preprint arXiv:1907.01160, 2019.
- M. Morrison and B. Pardo, “OtoMechanic: Auditory Automobile Diagnostics via Query-by-Example,” in Proceedings of the 2019 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2019), 2019.
- F. Pishdadian, B. Kim, P. Seetharaman, and B. Pardo, “Classifying Non-speech Vocals: Deep vs Signal Processing Representations,” in Proceedings of the 2019 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2019), 2019.
- P. Seetharaman, G. Wichern, J. Le Roux, and B. Pardo, “Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 356–360.
2018
- E. J. Humphrey et al., “An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music,” IEEE Signal Processing Magazine, vol. 36, no. 1, pp. 82–94, 2018.
- B. Pardo, Z. Rafii, and Z. Duan, “Audio source separation in a musical context,” in Springer Handbook of Systematic Musicology, Springer, Berlin, Heidelberg, 2018, pp. 285–298.
- M. Cartwright, B. Pardo, and G. J. Mysore, “Crowdsourced Pairwise-Comparison for Source Separation Evaluation,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 606–610.
- P. Seetharaman, G. J. Mysore, P. Smaragdis, and B. Pardo, “Blind Estimation of the Speech Transmission Index for Speech Quality Prediction,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 591–595.
- Z. Rafii, A. Liutkus, F.-R. Stoter, S. I. Mimilakis, D. FitzGerald, and B. Pardo, “An overview of lead and accompaniment separation in music,” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), vol. 26, no. 8, pp. 1307–1335, 2018.
- B. Kim and B. Pardo, “A human-in-the-loop system for sound event detection and annotation,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 8, no. 2, p. 13, 2018.
- B. Pardo, A. Liutkus, Z. Duan, and G. Richard, “Applying Source Separation to Music,” in Audio Source Separation and Speech Enhancement, 2018.
- Y. Zhang, B. Pardo, and Z. Duan, “Siamese Style Convolutional Neural Networks for Sound Search by Vocal Imitation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 2, pp. 429–441, 2018.
- J. Wilkins, P. Seetharaman, A. Wahl, and B. Pardo, “VocalSet: A Singing Voice Dataset,” in Proceedings of the 19th International Society of Music Information Retrieval Conference (ISMIR 2018), 2018.
- E. Manilow, P. Seetharaman, and B. Pardo, “The Northwestern University Source Separation Library,” in Proceedings of the 19th International Society of Music Information Retrieval Conference (ISMIR 2018), 2018.
- F. Pishdadian and B. Pardo, “Multi-Resolution Common Fate Transform,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 2, pp. 342–354, 2018.
- M. Mueller, B. A. Pardo, G. J. Mysore, and V. Valimaki, “Recent Advances in Music Signal Processing [From the Guest Editors],” IEEE Signal Processing Magazine, vol. 36, no. 1, pp. 17–19, 2018.
- K. Chao, P. Seetharaman, V. Tang, B. A. Pardo, and S. Van der Lee, “Automatic classification of triggered tectonic tremor with deep learning,” in AGU Fall Meeting Abstracts, 2018.
- V. Tang, P. Seetharaman, K. Chao, B. A. Pardo, and S. van der Lee, “Siamese networks for triggered earthquakes detection,” in AGU Fall Meeting Abstracts, 2018.
- B. Kim, M. Ghei, B. Pardo, and Z. Duan, “Vocal imitation set: a dataset of vocally imitated sound events using the audioset ontology,” in Proceedings of the 2018 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2018), 2018.
- B. Margolis, M. Ghei, and B. Pardo, “Applying triplet loss to siamese-style networks for audio similarity ranking,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018), 2018, pp. 128–132.
- B. Kim, “Leveraging user input and feedback for interactive sound event detection and annotation,” in 23rd International Conference on Intelligent User Interfaces, 2018, pp. 671–672.
2017
- P. Seetharaman and Z. Rafii, “Cover Song Identification with 2D Fourier Transform Sequences,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, 2017.
- F. Pishdadian, B. Pardo, and A. Liutkus, “A multi-resolution approach to common fate-based audio separation,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 566–570.
- M. Donovan, P. Seetharaman, and B. Pardo, “A Web Audio Node for the Fast Creation of Natural Language Interfaces for Audio Production,” 2017.
- A. Karp and B. Pardo, “HaptEQ: A Collaborative Tool For Visually Impaired Audio Producers,” in Proceedings of the 12th International Audio Mostly Conference on Augmented and Participatory Sound and Music Experiences, 2017, p. 39.
- P. Seetharaman, F. Pishdadian, and B. Pardo, “Music/voice separation using the 2d Fourier transform,” in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017, pp. 36–40.
- E. Manilow, P. Seetharaman, F. Pishdadian, and B. Pardo, “Predicting algorithm efficacy for adaptive multi-cue source separation,” in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017, pp. 274–278.
- E. Manilow and B. Pardo, “Leveraging repetition to do audio imputation,” in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017, pp. 309–313.
- B. Kim and B. Pardo, “I-sed: An interactive sound event detector,” in Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017, pp. 553–557.
2016
- M. Cartwright, B. Pardo, G. J. Mysore, and M. Hoffman, “Fast and easy crowdsourced perceptual audio evaluation,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 619–623.
- P. Seetharaman and B. Pardo, “Simultaneous Separation and Segmentation in Layered Music.,” in ISMIR, 2016, pp. 495–501.
- P. Seetharaman and B. Pardo, “Audealize: Crowdsourced audio production tools,” Journal of the Audio Engineering Society, vol. 64, no. 9, pp. 683–695, 2016.
- R. N. Brewer, M. Cartwright, A. Karp, B. Pardo, and A. M. Piper, “An approach to audio-only editing for visually impaired seniors,” in Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, 2016, pp. 307–308.
- B. Pardo, “Rethinking audio production tools,” The Journal of the Acoustical Society of America, vol. 140, no. 4, pp. 3090–3090, 2016.
2015
- M. Cartwright and B. Pardo, “Audio Production with Intelligent Machines,” in Workshop on “Collaborating with Intelligent Machines: Interfaces for Creative Sound” at the ACM Conference on Human Factors in Computing Systems (CHI), Seoul, Korea, 2015.
- F. J. Rodriguez-Serrano, Z. Duan, P. Vera-Candeas, B. Pardo, and J. J. Carabias-Orti, “Online score-informed source separation with adaptive instrument models,” Journal of New Music Research, vol. 44, no. 2, pp. 83–96, 2015.
- Z. Rafii, A. Liutkus, and B. Pardo, “A simple user interface system for recovering patterns repeating in time and frequency in mixtures of sounds,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp. 271–275.
- M. Cartwright and B. Pardo, “Vocalsketch: Vocally imitating audio concepts,” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015, pp. 43–46.
- J. Ford, M. Cartwright, and B. Pardo, “MixViz: A tool to visualize masking in audio mixes,” in Audio Engineering Society Convention 139, 2015.
2014
- Z. Rafii, B. Coover, and J. Han, “An audio fingerprinting system for live version identification using image processing techniques,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 644–648.
- A. Liutkus, Z. Rafii, B. Pardo, D. Fitzgerald, and L. Daudet, “Kernel Spectrogram models for source separation,” in 2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014, pp. 6–10.
- S. Ewert, B. Pardo, M. Müller, and M. D. Plumbley, “Score-informed source separation for musical audio recordings: An overview,” IEEE Signal Processing Magazine, vol. 31, no. 3, pp. 116–124, 2014.
- Z. Rafii, A. Liutkus, and B. Pardo, “REPET for background/foreground separation in audio,” in Blind Source Separation, Springer, Berlin, Heidelberg, 2014, pp. 395–411.
- D. FitzGerald, A. Liukus, Z. Rafii, B. Pardo, and L. Daudet, “Harmonic/percussive separation using kernel additive modelling,” 2014.
- A. Liutkus, D. Fitzgerald, Z. Rafii, B. Pardo, and L. Daudet, “Kernel additive models for source separation,” IEEE Transactions on Signal Processing, vol. 62, no. 16, pp. 4298–4310, 2014.
- Z. Duan, B. Pardo, and L. Daudet, “A novel cepstral representation for timbre modeling of sound sources in polyphonic mixtures,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 7495–7499.
- M. Cartwright and B. Pardo, “Synthassist: an audio synthesizer programmed with vocal imitation,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 741–742.
- M. Cartwright and B. Pardo, “SynthAssist: Querying an Audio Synthesizer by Vocal Imitation,” in International Conference on New Interfaces for Musical Expression, 2014.
- B. Kim and B. Pardo, “Adapting Collaborative Filtering to Personalized Audio Production,” in Second AAAI Conference on Human Computation and Crowdsourcing, 2014.
- P. Seetharaman and B. Pardo, “Reverbalize: a crowdsourced reverberation controller,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 739–740.
- Z. Rafii, Z. Duan, and B. Pardo, “Combining rhythm-based and pitch-based methods for background and melody separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 12, pp. 1884–1893, 2014.
- P. Seetharaman and B. Pardo, “Crowdsourcing a reverberation descriptor map,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 587–596.
- B. Kim and B. Pardo, “Speeding learning of personalized audio equalization,” in 2014 13th International Conference on Machine Learning and Applications, 2014, pp. 495–499.
- S. Eweret, B. Prado, M. Muller, and M. Plumbley, “Score-Informed Source separation for musical audio recordings,” IEEE Signal Proc. Magazine, vol. 116, p. 124, 2014.
- M. Cartwright and B. Pardo, “Translating sound adjectives by collectively teaching abstract representations,” in Proceedings of the Collective Intelligence Conference, 2014.
- M. Cartwright, B. Pardo, and J. Reiss, “Mixploration: Rethinking the audio mixer interface,” in Proceedings of the 19th international conference on Intelligent User Interfaces, 2014, pp. 365–370.
2013
- Z. Duan, J. Han, and B. Pardo, “Multi-pitch streaming of harmonic sound mixtures,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 1, pp. 138–150, 2013.
- Z. Rafii and B. Pardo, “Online REPET-SIM for real-time speech enhancement,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 848–852.
- M. D. Greenberg, B. Pardo, K. Hariharan, and E. Gerber, “Crowdfunding support tools: predicting success & failure,” in CHI’13 Extended Abstracts on Human Factors in Computing Systems, 2013, pp. 1815–1820.
- J. Springer, Z. Duan, and B. Pardo, “Approaches to multiple concurrent species bird song recognition,” in The 2nd International Workshop on Machine Listening in Multisource Environments, ICASSP, 2013.
- Z. Rafii, F. G. Germain, D. L. Sun, and G. J. Mysore, “Combining Modeling Of Singing Voice And Background Music For Automatic Separation Of Musical Mixtures.,” in ISMIR, 2013, vol. 10, pp. 645–680.
2012
- P. Seetharaman and S. P. Tarzia, “The Hand Clap as an Impulse Source for Measuring Room Acoustics,” in Audio Engineering Society Convention 132, 2012.
- J. Han, G. J. Mysore, and B. Pardo, “Audio imputation using the non-negative hidden markov model,” in International Conference on Latent Variable Analysis and Signal Separation, 2012, pp. 347–355.
- Z. Rafii and B. Pardo, “Music/Voice Separation Using the Similarity Matrix.,” in ISMIR, 2012, pp. 583–588.
- M. Cartwright and B. Pardo, “Building a music search database using human computation,” in Proceedings of the 9th Sound and Music Computing Conference (SMC 2012), Copenhagen, Denmark, 2012.
- B. Pardo, D. Little, and D. Gergle, “Towards speeding audio EQ interface building with transfer learning,” machine learning, vol. 10, p. 11, 2012.
- J. Han, G. J. Mysore, and B. Pardo, “Language informed bandwidth expansion,” in 2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012, pp. 1–6.
- M. B. Cartwright and B. Pardo, “Novelty measures as cues for temporal salience in audio similarity,” in Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies, 2012, pp. 51–56.
- Z. Rafii and B. Pardo, “Repeating pattern extraction technique (REPET): A simple method for music/voice separation,” IEEE transactions on audio, speech, and language processing, vol. 21, no. 1, pp. 73–84, 2012.
- B. Pardo, D. Little, and D. Gergle, “Building a personalized audio equalizer interface with transfer learning and active learning,” in Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies, 2012, pp. 13–18.
- A. Liutkus, Z. Rafii, R. Badeau, B. Pardo, and G. Richard, “Adaptive filtering for music/voice separation exploiting the repeating musical structure,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 53–56.
2011
- M. Cartwright, Z. Rafii, J. Han, and B. Pardo, “Making searchable melodies: Human vs. machine,” in Proc. of 3rd Human Computation Workshop (HCOMP 2011). San Francisco: AAAI Press, 2011.
- A. T. Sabin, Z. Rafii, and B. Pardo, “Weighted-function-based rapid mapping of descriptors to audio processing parameters,” Journal of the Audio Engineering Society, vol. 59, no. 6, pp. 419–430, 2011.
- Z. Duan and B. Pardo, “Soundprism: An online system for score-informed source separation of music audio,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 6, pp. 1205–1215, 2011.
- Z. Duan and B. Pardo, “A state space model for online polyphonic audio-score alignment,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 197–200.
- Z. Duan and B. Pardo, “Aligning Semi-Improvised Music Audio with Its Lead Sheet.,” in ISMIR, 2011, pp. 513–518.
- J. Han and B. Pardo, “Reconstructing completely overlapped notes from musical mixtures,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 249–252.
- M. B. Cartwright and B. Pardo, “An active learning-based interface for synthesizer programming,” The Journal of the Acoustical Society of America, vol. 130, no. 4, pp. 2432–2432, 2011.
- E. Scott, P. M. Silva, B. Pardo, and T. N. Pappas, “Adaptive user interfaces for relating high-level concepts to low-level photographic parameters,” in Human Vision and Electronic Imaging XVI, 2011, vol. 7865, p. 78650A.
- M. B. Cartwright, Z. Rafii, J. Han, and B. Pardo, “Making searchable melodies: Human versus machine,” in Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011.
- Z. Rafii and B. Pardo, “Degenerate unmixing estimation technique using the constant Q transform,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 217–220.
- D. Little, B. Pardo, and B. Wright, “A Computational Model of Auditory Perceptual Learning: Predicting Learning Interference Across Multiple Tasks,” in Proceedings of the Annual Meeting of the Cognitive Science Society, 2011, vol. 33, no. 33.
- M. Cartwright and B. Pardo, “Interactive Learning for Creativity Support in Music Production,” in Proceedings of the Semi-Automated Creativity Workshop, 2011, pp. 3–6.
- Z. Rafii and B. Pardo, “A simple music/voice separation method based on the extraction of the repeating musical structure,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 221–224.
2010
- Z. Duan, B. Pardo, and C. Zhang, “Multiple fundamental frequency estimation by modeling spectral peaks and non-peak regions,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 8, pp. 2121–2133, 2010.
- A. Huq, M. Cartwright, and B. Pardo, “Crowdsourcing a real-world on-line query by humming system,” in Proceedings of the Sixth Sound and Music Computing Conference (SMC 2010), 2010.
- Z. Duan, J. Han, and B. Pardo, “Song-level multi-pitch tracking by heavily constrained clustering,” in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, pp. 57–60.
- Z. Duan and B. Pardo, “A real-time score follower for Mirex 2010,” work, vol. 11, p. 12, 2010.
- J. Han and B. Pardo, “Reconstructing individual monophonic instruments from musical mixtures using scene completion.,” Journal of the Acoustical Society of America, vol. 128, no. 4, p. 2309, 2010.
- D. Little and B. Pardo, “Computational Models of Perceptual Learning Across Multiple Auditory Tasks: Modeling Daily Learning Limits as Memory Decay,” in 10th International Conference on Cognitive Modeling, 2010, p. 145.
2009
- Z. Rafii and B. Pardo, “A Digital Reverberator controlled through Measures of the Reverberation,” Northwestern University, EECS Department, Technical Report NWU-EECS-09-08, 2009.
- A. T. Sabin and B. Pardo, “A method for rapid personalization of audio equalization parameters,” in Proceedings of the 17th ACM international conference on Multimedia, 2009, pp. 769–772.
- J. Zujovic, L. Gandy, S. Friedman, B. Pardo, and T. N. Pappas, “Classifying paintings by artistic genre: An analysis of features & classifiers,” in 2009 IEEE International Workshop on Multimedia Signal Processing, 2009, pp. 1–5.
- A. T. Sabin and B. Pardo, “2DEQ: an intuitive audio equalizer,” in Proceedings of the seventh ACM conference on Creativity and cognition, 2009, pp. 435–436.
- J. Liu, L. Birnbaum, and B. Pardo, “Spectrum: Retrieving different points of view from the blogosphere,” in Third International AAAI Conference on Weblogs and Social Media, 2009.
- Z. Rafii and B. Pardo, “Learning to Control a Reverberator Using Subjective Perceptual Descriptors.,” in ISMIR, 2009, pp. 285–290.
- Z. Duan, J. Han, and B. Pardo, “A multi-pitch tracking system (mirex 2009),” Proceedings of the Fifth Music Information Retrieval Evaluation eXchange (MIREX 2009), Kobe.[cited at p. 172], 2009.
- B. Duane and B. Pardo, “Streaming from MIDI Using Constraint Satisfaction Optimization and Sequence Alignment.,” in ICMC, 2009.
- J. Han and B. Pardo, “Improving separation of harmonic sources with iterative estimation of spatial cues,” in 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2009, pp. 77–80.
- Z. Duan, J. Han, and B. Pardo, “Harmonically Informed Multi-Pitch Tracking.,” in ISMIR, 2009, pp. 333–338.
2008
- J. Moshier and B. Pardo, “A database for the accommodation of structural and stylistic variability in improvised jazz piano performances,” ISMIR, Late-Breaking/Demo Session, 2008.
- D. Little and B. Pardo, “Learning Musical Instruments from Mixtures of Audio with Weak Labels.,” in ISMIR, 2008, vol. 8, pp. 127–132.
- M. Skalak, J. Han, and B. Pardo, “Speeding Melody Search With Vantage Point Trees.,” in ISMIR, 2008, pp. 95–100.
- A. Sabin and B. Pardo, “Rapid learning of subjective preference in equalization,” in Audio Engineering Society Convention 125, 2008.
- J. Liu, L. Birnbaum, and B. Pardo, “Categorizing blogger’s interests based on short snippets of blog posts,” in Proceedings of the 17th ACM conference on Information and knowledge management, 2008, pp. 1525–1526.
- B. Pardo, D. Little, R. Jiang, H. Livni, and J. Han, “The vocalsearch music search engine,” in Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, 2008, pp. 430–430.
- D. A. Shamma, J. Woodruff, and B. Pardo, “MusicStory: An Autonomous, Personalized Music Video Creator,” in Intelligent Music Information Systems: Tools and Methodologies, IGI Global, 2008, pp. 289–304.
- Y. Gao et al., “Image spam hunter,” in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, pp. 1765–1768.
2007
- B. A. Pardo, “Design considerations for technology to support music improvisation,” in Proceedings of 6th Creativity and Cognition Conference Workshop on Supporting Creative Acts Beyond Dissemination, 2007.
- B. Fox, A. Sabin, B. Pardo, and A. Zopf, “Modeling perceptual similarity of audio signals for blind source separation evaluation,” in International Conference on Independent Component Analysis and Signal Separation, 2007, pp. 454–461.
- D. Little, D. Raffensperger, and B. Pardo, “A Query by Humming System that Learns from Experience.,” in ISMIR, 2007, pp. 335–338.
- B. Pardo and D. Shamma, “Teaching a music search engine through play,” Chi 2007, computer/human interaction, 2007.
- D. Little, D. Raffensperger, and B. Pardo, “Online Training of a Music Search Engine,” 2007.
- D. Little, D. Raffensperger, and B. Pardo, “User specific training of a music search engine,” in International Workshop on Machine Learning for Multimodal Interaction, 2007, pp. 72–83.
- B. Fox and B. Pardo, “Towards a model of perceived quality of blind audio source separation,” in 2007 IEEE International Conference on Multimedia and Expo, 2007, pp. 1898–1901.
- N. Nichols, J. Liu, B. Pardo, K. Hammond, and L. Birnbaum, “Learning to gesture: applying appropriate animations to spoken text,” in Proceedings of the 15th ACM international conference on Multimedia, 2007, pp. 827–830.
- R. B. Dannenberg, W. P. Birmingham, B. Pardo, N. Hu, C. Meek, and G. Tzanetakis, “A comparative evaluation of search techniques for query-by-humming using the MUSART testbed,” Journal of the American Society for Information Science and Technology, vol. 58, no. 5, pp. 687–701, 2007.
2006
- B. Pardo, “Session details: Music information retrieval,” Communications of the ACM, vol. 49, no. 8, 2006.
- W. Birmingham, R. Dannenberg, and B. Pardo, “Query by humming with the vocalsearch system,” Communications of the ACM, vol. 49, no. 8, pp. 49–52, 2006.
- B. Pardo, “Music information retrieval,” Communications of the ACM, 2006.
- J. Woodruff and B. Pardo, “Using pitch, amplitude modulation, and spatial cues for separation of harmonic instruments from stereo music recordings,” EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, p. 086369, 2006.
- B. Pardo, “Finding structure in audio for music information retrieval,” IEEE Signal Processing Magazine, vol. 23, no. 3, pp. 126–132, 2006.
- D. A. Shamma and B. Pardo, “Karaoke callout: using social and collaborative cell phone networking for new entertainment modalities and data collection,” in Proceedings of the 1st acm workshop on audio and music computing multimedia, 2006, pp. 133–136.
- J. Woodruff and B. Pardo, “Active source estimation for improved source separation,” in Northwestern University, EECS Dept, 2006.
- J. F. Woodruff, B. Pardo, and R. B. Dannenberg, “Remixing stereo music with score-informed source separation.,” in ISMIR, 2006, pp. 314–319.
2005
- B. Pardo and M. Sanghi, “Polyphonic Musical Sequence Alignment for Database Search.,” in ISMIR, 2005, pp. 215–222.
- B. Pardo and W. Birmingham, “Modeling form for on-line following of musical performances,” in Proceedings of the National Conference on Artificial Intelligence, 2005, vol. 20, no. 2, p. 1018.
- D. A. Shamma, B. Pardo, and K. J. Hammond, “Musicstory: a personalized music video creator,” in Proceedings of the 13th annual ACM international conference on Multimedia, 2005, pp. 563–566.
- B. A. Pardo, “Probabilistic sequence alignment methods for on-line score following of music performances,” PhD thesis, University of Michigan, 2005.
2004
- R. B. Dannenberg, W. P. Birmingham, G. Tzanetakis, C. Meek, N. Hu, and B. Pardo, “The MUSART testbed for query-by-humming evaluation,” Computer Music Journal, vol. 28, no. 2, pp. 34–48, 2004.
- B. Pardo, J. Shifrin, and W. Birmingham, “Name that tune: A pilot study in finding a melody from a sung query,” Journal of the American Society for Information Science and Technology, vol. 55, no. 4, pp. 283–300, 2004.
- B. Pardo, “Tempo Tracking with a Single Oscillator.,” in ISMIR, 2004.
2003
- B. Pardo and W. P. Birmingham, “Query by humming: How good can it get,” The MIR/MDL Evaluation Project White Paper Collection, Edition, vol. 3, pp. 107–109, 2003.
- C. Menezes, B. Pardo, D. Erickson, and O. Fujimura, “Changes in syllable magnitude and timing due to repeated correction,” Speech Communication, vol. 40, no. 1-2, pp. 71–85, 2003.
- B. A. Pardo, W. Birmingham, C. Meek, K. O’Malley, and J. Shifrin, “Music Information Retrieval Systems: Dr. Dobbs Journal,” 2003.
2002
- B. Pardo and W. Birmingham, “Improved Score Following forAcoustic Performances,” 2002.
- J. Shifrin, B. Pardo, C. Meek, and W. Birmingham, “HMM-based musical query retrieval,” in Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries, 2002, pp. 295–300.
- B. Pardo and W. P. Birmingham, “Encoding Timing Information for Musical Query Matching,” in ISMIR, 2002.
- W. Birmingham, B. Pardo, C. Meek, and J. Shifrin, “The MusArt music-retrieval system: An overview,” D-lib Magazine, vol. 8, no. 2, 2002.
- B. Pardo and W. P. Birmingham, “Improved Score Following for Acoustic Performances.,” in ICMC, 2002.
- B. Pardo, C. Meek, and W. Birmingham, “Comparing aural music-information retrieval systems,” in Workshop on the Creation of Standardized Test Collections, Tasks, and Metrics for Music Information Retrieval (MIR) and Music Digital Library (MDL) Evaluation. Second Joint Conference on Digital Libraries. Portland, OR, 2002, pp. 39–41.
- C. Menezes, D. Erickson, J. McGory, B. Pardo, and O. Fujimura, “An articulatory and perceptual study of phrasing,” in ISCA Tutorial and Research Workshop (ITRW) on Temporal Integration in the Perception of Speech, 2002.
- B. Pardo and W. P. Birmingham, “Algorithms for chordal analysis,” Computer Music Journal, vol. 26, no. 2, pp. 27–49, 2002.
2001
- B. Pardo and W. P. Birmingham, “The chordal analysis of tonal music,” The University of Michigan, Department of Electrical Engineering and Computer Science Technical Report CSE-TR-439-01, 2001.
- B. Pardo and W. P. Birmingham, “Following a musical performance from a partially specified score,” in Proceedings of the 2001 Multimedia Technology and Applications Conference, 2001, pp. 202–207.
2000
- B. Pardo and W. P. Birmingham, “Automated partitioning of tonal music,” in FLAIRS Conference, 2000, pp. 23–27.
- C. J. Mitchell, C. Menezes, J. C. Williams, B. Pardo, D. Erickson, and O. Fujimura, “Changes in syllable and boundary strengths due to irritation,” in ISCA Tutorial and Research Workshop (ITRW) on Speech and Emotion, 2000.
- B. Pardo and O. Fujimura, “Syllable-boundary magnitudes from jaw movement patterns,” The Journal of the Acoustical Society of America, vol. 107, no. 5, pp. 2905–2905, 2000.
- B. A. Pardo and W. Birmingham, “On the computational properties of harmonic analysis,” in Workshop on Artificial Intelligence and Music, AAAI 2000, 2000.
1998
- D. Erickson, O. Fujimura, and B. Pardo, “Articulatory correlates of prosodic control: Emotion and emphasis,” Language and Speech, vol. 41, no. 3-4, pp. 399–417, 1998.
- O. Fujimura, B. Pardo, and D. Erickson, “Effect of emphasis and irritation on jaw opening,” in Proc. ESCA, 1998, pp. 23–29.