Menachem Rafaelof

Education

• M.S., Mechanical Engineering, University of Utah, 1987
• B.S., Mechanical Engineering, Brigham Young University, 1984

Work Experience

• Principal Engineer Assoc., National Institute of Aerospace, 2014-present
• Principal Engineer, Seagate Technology, 1999-2014
• Principal Investigator and Project manager, MJR Research Corp., 1995-1999
• Design/Research, SARCOS Research Corp., 1990-1997
• Design/Research Engineer, Center for Engineering Design, University of Utah, 1987-1998

Research Areas/Expertise

• Modeling of human auditory system
• Psychoacoustics, audibility, statistics and machine learning
• Design for low machine sound and vibration
• Signal processing, DSP, controls, system ID
• Development of MEMS sensors and actuators

Current Research

Annoyance of Noise

Precisely relating human response to noise and its causes is extremely valuable for the design of systems that limit the adverse effects of noise.  The goal of this effort is precise prediction of the perception of noise (e.g. its annoyance) which may be used to guide the design and operation of many machines and systems.

Audibility

With the advent of Unmanned Arial (UA) and Urban Aerial Mobility (UAM) vehicles, there is a major concern about the impact of noise by these vehicles on public. The goal of this work is the development of algorithms, based on models of human auditory system, to assess the audibility of a given vehicle noise over ambient sound encountered in different urban areas.

System Identification

Precise reproduction of sound by helicopters and Unmanned Arial (UA) and Urban Aerial Mobility (UAM) vehicles could help engineers to improve their acoustic emission. System identification is a new approach to model the dynamics of source responsible for acoustic emission. This approach could result in more accurate reproduction of sound while pointing to design parameters that may be altered to lower acoustic emission.

Publications

1. NASA TM-2018-220120: “Characterization of Low Frequency Auditory Filters”, Rafaelof Menachem, Christian W. Andrew, Shepherd P. Kevin, Rizzi A. Stephen and Stephenson H. James, Langley Research Center, Hampton, VA, December 2018.
2. NASA TM-2018-220121: “Implementation of Revised Auditory Filters for IHEARDIT”, Rafaelof Menachem, Christian W. Andrew, Shepherd P. Kevin, Smith D. Charles, Stephenson H. James and Rizzi A. Stephen, Langley Research Center, Hampton, VA, December 2018.
3. Rafaelof Menachem and Schroeder Andrew, “Investigation of Machine Learning Algorithms to Model Perception of Sound”, The Journal of the Acoustical Society of America 143, 1741 (2018); https://doi.org/10.1121/1.5035683
4. Rizzi A. Stephen, Christian W. Andrew and Rafaelof Menachem, “A Laboratory Method for Assessing Audibility and Localization of Rotorcraft Fly-In Noise” AHS Journal paper, 2018, USA.
5. Rizzi A. Stephen, Palumbo L. Daniel, Rathsam Jonathan, Christian W. Andrew and Rafaelof Menachem, “Annoyance to Noise Produced by a Distributed Electric Propulsion High-Lift System” AIAA Aviation 5-9 June 2017, Denver, CO.

Rafaelof Menachem, “A model to gauge the annoyance due to arbitrary time-varying sound,” Paper 68, NOISE-CON 2016, Providence, RI, 2016.