Mark Cartwright

Mark Cartwright
Assistant Professor, Informatics
973-596-3388
3902E Guttenberg Information Technologies Center (GITC)
About Me
Mark Cartwright comes to NJIT from New York University, where he was a research assistant professor in the Department of Computer Science and Engineering, with affiliations to the Music and Audio Research Lab and the Center for Urban Science and Progress. His research lies at the intersection of human-computer interaction and machine learning applied to music and environmental audio in a field called machine listening. This field, the auditory sibling to computer vision, aims to endow machines with the ability to perceive and understand sound as humans do. It has applications such as assistive hearing devices for the hearing impaired, environmental acoustic monitoring, bioacoustics monitoring, music search / recommendation, intelligent media production tools, autonomous vehicle sensing, and more. Within this field, his work focuses on interactive solutions to problems, in which the user plays an active role in teaching the machine to understand particular audio concepts. During his PhD, he leveraged machine listening to develop easier-to-use interfaces for sound design and music production tools, e.g a synthesizer that you control by imitating sounds with your voice. This work enables novice users to use complex audio tools for creative expression that typically require significant knowledge and experience to use effectively. Recently, he has taken part in the Sounds of New York City, a large National Science Foundation (NSF)-funded project that leverages an acoustic sensor network along with citizen science and machine listening to monitor, analyze and mitigate urban noise pollution. This project aims to build tools that can measure the impact of different types of sounds (e.g. jackhammers) on the city’s noise pollution and create real-time alerts so that city agencies can respond more effectively.
Education
Ph.D.; Northwestern University; Computer Science; 2016

M.A.; Stanford University; Music Science and Technology; 2007

B.M.; Northwestern University; Music Technology; 2004

Website
http://www.markcartwright.com
2024 Spring Courses
CS 488 - INDEPENDENT STUDY IN CS

IS 488 - INDEPENDENT STUDY IN INFO

IS 701B - MASTER'S THESIS

IS 726 - INDEPENDENT STUDY II

IS 776 - IS RESEARCH STUDY

IS 790A - DOCT DISSERTATION & RES

IS 792 - PRE-DOCTORAL RESEARCH

IS 489 - INFO UNDERGRAD THESIS RESEARCH

IS 700B - MASTER'S PROJECT

IS 725 - INDEPENDENT STUDY I

Teaching Interests
machine listening, interactive machine learning, audio processing, multimedia computing
Past Courses
CS 485: SELECTED TOPICS IN CS

CS 485: ST: MACHINE LISTENING

CS 698: ST: MACHINE LISTENING

IS 247: DESIGNING THE USER EXPERIENCE

IS 485: SPECIAL TOPICS IN INFORMATION SYSTEMS

IS 485: SPECIAL TOPICS IN IS - I

IS 485: ST: MACHINE LISTENING

IS 657: SPATIOTEMPORAL URBAN ANALYTICS

IS 698: ST: SPECIAL PROJECTS

Research Interests
machine listening, human computer interaction, machine learning, audio, music, crowdsourcing, creativity support tools, interactive machine learning, music information retrieval, digital signal processing
Conference Proceeding
Multi-label Open-set Audio Classification
Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), September 2023

Does a quieter city mean fewer complaints? The Sounds of New York City During COVID-19 Lockdown
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2023

A Study on Robustness to Perturbations for Representations of Environmental Sound
August 2022

Urban Rhapsody: Large-scale exploration of urban soundscapes
June 2022

How people who are deaf, Deaf, and hard of hearing use technology in creative sound activities
ACM, October (4th Quarter/Autumn) 2022

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Conference Abstract
A retrospective on monitoring noise pollution with machine learning in the Sounds of New York City project
Journal of the Acoustical Society of America, May 2023

Journal Article
Mendez Mendez, Ana , & Cartwright, Mark, & Bello, Juan Pablo, & Nov, Oded (2022). Eliciting Confidence for Improving Crowdsourced Audio Annotations. ACM, 6(CSCW1),

Pardo, Bryan, & Cartwright, Mark, & Seetharaman, Prem, & Kim, Bongjun (2019). Learning to Build Natural Audio Production Interfaces. Arts, 8(3), 110.

McFee, Brian, & Kim, Jong Wook, & Cartwright, Mark, & Salamon, Justin, & Bittner, Rachel M., & Bello, Juan Pablo (2019). Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research. IEEE Signal Processing Magazine, 36(1), 128--137.

Lostanlen, Vincent, & Salamon, Justin, & Cartwright, Mark, & McFee, Brian, & Farnsworth, Andrew, & Kelling, Steve, & Bello, Juan Pablo (2019). Per-Channel Energy Normalization: Why and How. IEEE Signal Processing Letters, 26(1), 39--43.

Cartwright, Mark, & Seals, Ayanna, & Salamon, Justin, & Williams, Alex, & Mikloska, Stephanie, & MacConnell, Duncan, & Law, Edith, & Bello, Juan Pablo, & Nov, Oded (2017). Seeing Sound: Investigating the Effects of Visualizations and Complexity on Crowdsourced Audio Annotations.