Charlie Mydlarz
Research Assistant Professor

I’m a resourceful acoustician/engineer who designs, develops, and deploys advanced IoT devices to improve our understanding of: acoustic environments, machinery health, and urban conditions.


I design, implement, and deliver end-to-end systems to tackle different challenges, including: city-scale urban noise sensing, acoustic condition monitoring, urban flood detection, global soundscape perception, and urban mobility. In developing these solutions I’ve managed multi-disciplinary teams with high diversity of skills and experience, and successfully collaborated with stakeholders and partners such as: city agency personnel, urban residents, product development agencies, media agencies, and electrical fabrication houses. I’m able to deliver on projects requiring a high level of technical expertise and management.

I’m currently at the NYU Center for Urban Science and Progress (CUSP) and the Music and Audio Research Lab (MARL).



I lead the hardware and systems team on the Sounds Of New York City or SONYC project. I designed, fabricated and deployed a network of over 100 low-cost acoustic sensors across NYC, many of which have been running for over five years, to provide continuous, real-time, and accurate monitoring of urban noise. I designed and supervised the building of the back and front end systems for a scalable and resilient solution. This has allowed me to carry out large-scale analysis of urban noise activity to reveal patterns across space and time. These platforms have been used to create a data-driven approach for city agencies tasked with noise mitigation by feeding relevant, actionable, and timely noise information.


I developed, sourced, and deployed a network of domestically deployed noise sensors, employing design for manufacture (DFM) principles. This was in collaboration with LOFT LLC, a multidisciplinary product development studio. The sensors are mounted on window exteriors of NYC residents with acute noise problems. These advanced devices deliver accurate decibel measurements and machine learning driven sound source identification of noise events to city agencies tasked with enforcing noise violations. Residents engage with sensor data through app based visualizations of time series decibel data, enhanced noise reporting, and human-in-the-loop machine learning. To monitor air/noise health impacts on at-risk communities in NYC, I extended this new system with a highly accurate air quality sensor with deployments in NYC Chinatowns.


I lead the sensing research and development on this USDOT and ESD funded project developing novel sensing solutions for realtime urban flood monitoring. The project is partnered with a number of other institutions and city agencies with our group’s website here ( In New York City, sea-level rise has led to a dramatic increase in flood risk, particularly in low-lying and coastal neighborhoods. High-intensity rain storms compound this risk by conveying large volumes of water to drains, leading to backups and overflows. The resulting presence of standing water on streets and sidewalks can impede mobility and restrict access to transportation. Urban flood water can also contain a diverse array of contaminants, including fuels, raw sewage, and industrial/household chemicals. Access to real-time information on flooding can improve resiliency and efficiency by allowing residents to identify navigable transportation routes and make informed decisions to avoid exposure to flood water contaminants. Flood data can also aid city agencies in delineating areas to target for flood control improvements and making data-driven decisions on flood management. However, very little data exist on the frequency and extent of urban surface flooding, and there is an unmet need for hyperlocal information on the presence and depth of street-level flood water.


The Reconfigurable Environmental Intelligence Platform (REIP) significantly enhances the capabilities of remote sensor networks. I lead the hardware and systems design of this novel sensor system that makes use of a set of hardware modules that connect together to form an advanced sensing solution. Each sensing module will come in a number of variants allowing the end user to find the proper tradeoff between complexity/cost and power/features. The system uses a reconfigurable combination of: high resolution cameras, a 15 channel microphone array, environmental sensors, and cutting edge machine learning leveraging sensor fusion to deliver insight on environments for various applications. Our test cases include: indoor sensing for reducing waste in HVAC (heating, ventilating, and air conditioning) systems, and the monitoring of urban road/pedestrian traffic. The system has the ability to bring down remote sensor network deployment times from years to weeks.


I was co-founder of a startup concept that aimed to provide cutting edge ultra-wideband acoustic condition monitoring solutions to manufacturing machinery OEMs who rely on continuous mechanical operation and timely, actionable foresight of impending machinery faults. Early stage machinery malfunction can be indicated by abnormal acoustic emissions, such as those produced by bearing faults, slipping belts, or loose chains. Reactive, late-stage fault detection leading to machinery failure results in high parts/repair costs, and extensive losses due to longer periods of unscheduled downtime. Predictive and data-driven, early-stage fault detection allows for strategic planning of machinery repairs including more efficient use of scheduled downtime periods resulting in increased overall asset uptime. We made use of our experience in acoustic remote sensing, digital signal processing (DSP), and machine learning to enable us to extract insight from very noisy signals.

Sound Around You

My citizen science driven PhD project where people across the world were prompted to use their smartphone or audio recorder to capture clips from different sound environments, or soundscapes. These clips were uploaded to our online map, along with people’s opinions of them and why they chose to record it. My thesis investigated how sounds in our everyday environment make us feel. I designed and built the full-stack system including all web content and native apps.


Smart Cities IoT
• Remote sensing
• Big data collection, processing and analysis
• Electrical engineering including: small-scale SMT PCB design, bring-up, and reworking
• Resilient hardware design and fabrication
• Full stack baremetal Linux systems engineering, admin, and dev-ops
• Networking technologies and implementations
• City agency and partner liaison
• Report creation and dissemination to stakeholders
• Trying not to complain about the cold

• Laboratory and field measurement
• Digital signal processing
• Large scale acoustic data processing and analysis
• Embedded audio system design
• MEMS microphones
• Structure-borne sound measurement
• Calibration techniques
• Acoustic ecology

Management and planning
• Planning, organization, and delivery of 8 projects of varying sizes and team compositions
• Management of multi-disciplinary teams with high diversity of skills and experience
• Grant proposal writing on 6 successfully funded National Science Foundation projects totaling over $8M
• Conflict resolution in various team scenarios

• Research project conception, development and management
• Academic publishing in peer reviewed conferences and journals
• Grant proposal writing and budgeting
• Mentoring of over 50 students from high school to postdoctoral levels

• Customer discovery
• Business model creation and analysis
• Development of evidence-based commercialization strategies

Tools and languages
• Python: num/scipy, pandas, sklearn, librosa, tornado, multiprocessing, matplotlib, jupyter notebooks
• Ansible
• Docker, including: kubernetes and compose
• Shell scripting
• Objective C, swift and android java
• C, C++, C#
• Javascript, PHP, HTML, CSS
• Matlab
• Fusion 360
• Eagle, KiCad
• Photoshop, Illustrator, Premiere, InDesign, Audition


Aug 2019 – Now

New York University

Developed, sourced, and deployed a network of domestically deployed noise sensors, employing design for manufacture (DFM) principles. This was in collaboration with LOFT LLC, a multidisciplinary product development studio. To monitor air/noise health impacts on at-risk communities in NYC, I extended this new system with a highly accurate air quality sensor with deployments in NYC Chinatowns. This period has seen $2M in successful grant funding including an exciting project called FloodNet that had me build a new team and develop an ultra-low-power, LoRaWAN based flood monitoring sensor network for urban street flooding. This system is actively used by city agencies and government weather services to monitor and analyze flood conditions in realtime for emergency alerting and flood model validation. I have since completed its design for manufacture (DFM) process to scale up production to 500 deployed devices.

Sep 2016 – Aug 2019

New York University

Continued the development and expansion of the SONYC sensor network, one of the worlds largest and longest operating urban noise monitoring networks. I built up a diverse team of scientists and engineers to implement dev-ops solutions to the network backend and sensor provisioning. My work with city agencies increased as I tailored and integrated our solutions into their operational procedures. This position also saw the award of $2.6M in grants, which had me leading a hardware/software team developing a multi-modal sensor system incorporating 15 channel MEMS microphone arrays, dual high res cameras and a GPU equipped compute core for edge intelligence creation. In addition, I co-founded a startup concept for acoustic condition monitoring of manufacturing machinery including intense customer discovery and business model development.

Oct 2013 – Sep 2016

New York University

Brought on as one of the first postdocs to the Center for Urban Science and Progress (CUSP) to develop novel sensor systems for the monitoring of urban noise. I created a set of MEMS microphone based prototype urban noise sensors as part of the “Noise Project”. This early project success led to a $4.6M National Science Foundation (NSF) award for the Sounds Of New York City or SONYC project. During this period, I was the sole hardware research & development engineer on the project, designing and implementing a deployed fleet of 75 sensors mounted at strategic acoustic locations across NYC. This included the implementation of a full stack client-server infrastructure for robust data collection and network analysis. For the sensor’s custom MEMS microphone module, I designed, prototyped and liaised with fabrication houses in China to have 500 modules assembled to an exacting standard for high audio quality and module consistency.

Jan 2013 – Jun 2013

University of Salford

Framework for Innovation and Research in MediaCityUK (FIRM), Hyperlocal TV research project investigating hyperlocal TV services, with a particular focus on sports coverage. The project is partnered with BT, and a number of local charities and media organisations. The main aim is to gauge audience appreciation towards hyperlocal content and the ways in which they consume it.

Feb 2010 – Jul 2012

University of Salford

Research Assistant

EPSRC funded project ISESS (Identifying a Sound Environment for Secondary Schools): a three year research project investigating the effects on teaching and learning of different acoustic designs within secondary schools and classrooms.

Sep 2007 – Feb 2013

University of Salford

Enabling public engagement in a large-scale mass participation soundscape study utilizing smart phones for global subjective and objective data collection and analysis.

Sep 2005 – Sep 2006

HHB Professional Audio Products

In charge of the service and repair of professional audio equipment, including all microphones and CD burners. Ran all of the companies pro audio CD media testing and handled phone and email technical support for industry professionals.

Select publications

MDPI Sensors Journal, 2019 (special issue on Intelligent Sensors)

Presented at Internoise 2017 after peer review – Hong Kong

Applied Acoustics, special issue on Acoustics of Smart Cities, 2016

Awarded best paper at the 10th European Congress and Exposition on Noise Control Engineering (EuroNoise), Maastricht, The Netherlands, May 2015

Audio Engineering Society (AES) Convention 137, Los Angeles, USA, October 2014

Audio Engineering Society (AES) Convention 137, Los Angeles, USA, October 2014

The Journal of the Acoustical Society of America 137.1 (2015): 177-188

The Journal of the Acoustical Society of America 132.3 (2012): 2045-2045

INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Vol. 2011. No. 7. Osaka, Japan. Institute of Noise Control Engineering, 2011