Thesis

[pdf] [demo] Seetharaman, P. (2019). Bootstrapping the Learning Process for Computer Audition (PhD thesis). Northwestern University.

Patents

Prem Seetharaman, Kundan Kumar. Training machine learning frameworks to generate studio-quality recordings through manipulation of noisy audio signals. US Patent App. 18/154,707, July 2023

Prem Seetharaman, Kundan Kumar. Approaches to generating studio-quality recordings through manipulation of noisy audio. US Patent App. 18/154,718, July 2023.

Prem Seetharaman, Gautham J Mysore, and Bryan A Pardo. Sound Quality Prediction and Interface to Facilitate High-Quality Voice Recordings. US Patent App. 16/296,122. Sept. 2020.

Rafii, Z., & Seetharaman, P. (2018). Audio Identification Based on Data Structure.

Cremer, M. K., Rafii, Z., Coover, R., & Seetharaman, P. (2018). Automated Cover Song Identification.

Refereed Conference Papers

[pdf] [demo] [code] [examples] Hugo Flores Garcia, Prem Seetharaman, Rithesh Kumar, Bryan Pardo. VampNet: Music Generation via Masked Acoustic Token Modeling. In Proceedings of the 24th International Society for Music Information Retrieval Conference. November 2023.

[pdf] Liu, A., Seetharaman, P., & Pardo, B. (2020). Model Selection for Deep Audio Source Separation via Clustering Analysis. Proceedings of DCASE 2020. Best Student Paper.

[pdf] Manilow, E., Seetharaman, P., & Pardo, B. (2020). Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments. ArXiv Preprint ArXiv:1910.12621.

[pdf] Seetharaman, P., Wichern, G., Roux, J. L., & Pardo, B. (2020). Bootstrapping Deep Music Separation from Primitive Auditory Grouping Principles. ArXiv Preprint ArXiv:1910.11133.

[pdf] Seetharaman, P., Mysore, G., Pardo, B., Smaragdis, P., & Gomes, C. (2019). VoiceAssist: Guiding Users to High-Quality Voice Recordings. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM.

[pdf] [data] Manilow, E., Wichern, G., Seetharaman, P., & Roux, J. L. (2019). Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[pdf] Pishdadian, F., Kim, B., Seetharaman, P., & Pardo, B. (2019). Classifying Non-Speech Vocals: Deep vs Signal Processing Representations. Proceedings of DCASE 2019.

[pdf] Seetharaman, P., Wichern, G., Roux, J. L., & Pardo, B. (2019). Bootstrapping Single-Channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[pdf] Seetharaman, P., Wichern, G., Venkataramani, S., & Roux, J. L. (2019). Class-Conditional Embeddings for Music Source Separation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[pdf] [code] Manilow, E., Seetharaman, P., & Pardo, B. (2018). The Northwestern University Source Separation Library. In Proceedings of the 19th International Society for Music Information Retrieval Conference.

[pdf] Seetharaman, P., Mysore, G. J., Smaragdis, P., & Pardo, B. (2018). Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[pdf] [data] [demo] Wilkins, J., Seetharaman, P., Wahl, A., & Pardo, B. (2018). VocalSet: A Singing Voice Dataset. In Proceedings of the 19th International Society for Music Information Retrieval Conference.

[pdf] [demo] Seetharaman, P., Pishdadian, F., & Pardo, B. (2017). Music/Voice Separation Using the 2D Fourier Transform. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[pdf] [demo] Manilow, E., Seetharaman, P., Pishdadian, F., & Pardo, B. (2017). Predicting Algorithm Efficacy for Adaptive Multi-Cue Source Separation. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[pdf] [code] Seetharaman, P., & Rafii, Z. (2017). Cover Song Identification with 2D Fourier Transform Sequences. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[pdf] [data] Zheng, T., Seetharaman, P., & Pardo, B. (2016). SocialFX: Studying a Crowdsourced Folksonomy of Audio Effects Terms. In Proceedings of the 2016 ACM on Multimedia Conference (pp. 182–186). ACM.

[pdf] [code] Seetharaman, P., & Pardo, B. (2016). Simultaneous Separation and Segmentation in Layered Music. In Proceedings of the 17th International Society for Music Information Retrieval Conference (pp. 495–502).

[pdf] Seetharaman, P., & Pardo, B. (2014). Crowdsourcing a Reverberation Descriptor Map. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 587–596). ACM.

[pdf] Seetharaman, P., & Tarzia, S. P. (2012). The Hand Clap as an Impulse Source for Measuring Room Acoustics. In Audio Engineering Society Convention 132. Audio Engineering Society.

Refereed Journal Articles

[pdf] [code] Tang, V., Seetharaman, P., Chao, K., Pardo, B., & van der Lee, S. (2020). Automating the Detection of Dynamically Triggered Earthquakes via a Deep Metric Learning Algorithm. Seismological Research Letters.

[pdf] Pardo, B., Cartwright, M., Seetharaman, P., & Kim, B. (2019). Learning to Build Natural Audio Production Interfaces. In Arts (Vol. 8, p. 110). Multidisciplinary Digital Publishing Institute.

[pdf] Humphrey, E. J., Reddy, S., Seetharaman, P., Kumar, A., Bittner, R. M., Demetriou, A., … others. (2018). 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, 36(1), 82–94.

[pdf] [demo] Seetharaman, P., & Pardo, B. (2016). Audealize: Crowdsourced Audio Production Tools. Journal of the Audio Engineering Society, 64(9), 683–695.

Refereed Extended Abstracts

[pdf] [code] Donovan, M., Seetharaman, P., & Pardo, B. (2017). A Web Audio Node for the Fast Creation of Natural Language Interfaces for Audio Production.

[pdf] Seetharaman, P., & Pardo, B. (2014). Reverbalize: A Crowdsourced Reverberation Controller. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 739–740). ACM.

Non-refereed Abstracts

Chao, K., Seetharaman, P., Tang, V., Pardo, B. A., & Van der Lee, S. (2018). Automatic classification of triggered tectonic tremor with deep learning. In AGU Fall Meeting Abstracts.

Tang, V., Seetharaman, P., Chao, K., Pardo, B. A., & van der Lee, S. (2018). Siamese networks for triggered earthquakes detection. In AGU Fall Meeting Abstracts.