About

Mounya Elhilali, Charles Renn Faculty Scholar and Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, is recognized for her work in understanding how the human brain and machines process complex sounds. She founded the Laboratory for Computational Audio Perception (LCAP) and her research bridges neuroscience and audio technologies by examining the computational and neural bases of sound and speech perception and behavior in complex acoustic environments. Using mathematical signal processing models, behavioral testing, and neural recordings, she focuses on decoding how these processes guide human behavior and on engineering more efficient machine parsing of complex soundscapes. Her work has applications across medical, commercial, military, and robotic domains. Recently, she has explored how attention to sound provides feedback to brain networks and influences how humans analyze and understand their acoustic surroundings. Her research models the brain as an adaptive system that constantly changes its processing to sift through environmental sounds, offering new theories for advancing intelligent audio technologies. Her multidisciplinary approach has generated insights into brain sciences, adaptive signal processing, audio technologies, and medical systems, including developing new diagnostic technologies that leverage body sounds to address public health issues such as pneumonia. Elhilali is affiliated with Johns Hopkins’ Center for Language and Speech Processing and is a member of several professional societies, including IEEE, ASA, SfN, ARO, ISCA, AWIS, and ASEE. She has received numerous awards, including the Johns Hopkins Catalyst Award, Kenan Award for Innovative Projects in Undergraduate Education, Office of Naval Research Young Investigator Award, and a National Science Foundation Early Career Award. Her educational background includes a BS in Software Engineering from Al Akhawayn University and an MS and PhD in Electrical and Computer Engineering from the University of Maryland.

Research topics

• Artificial Intelligence
• Computer Science
• Speech recognition
• Acoustics
• Medicine
• Psychology
• Biology
• Internal medicine
• Pediatrics
• Communication
• Cognitive psychology
• Neuroscience
• Real-time computing
• Telecommunications
• Computer hardware
• Surgery

Selected publications

• Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings

  IEEE Journal of Biomedical and Health Informatics · 2021 · 47 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Chest auscultation is a widely used clinical tool for respiratory disease detection. The stethoscope has undergone a number of transformative enhancements since its invention, including the introduction of electronic systems in the last two decades. Nevertheless, stethoscopes remain riddled with a number of issues that limit their signal quality and diagnostic capability, rendering both traditional and electronic stethoscopes unusable in noisy or non-traditional environments (e.g., emergency rooms, rural clinics, ambulatory vehicles). This work outlines the design and validation of an advanced electronic stethoscope that dramatically reduces external noise contamination through hardware redesign and real-time, dynamic signal processing. The proposed system takes advantage of an acoustic sensor array, an external facing microphone, and on-board processing to perform adaptive noise suppression. The proposed system is objectively compared to six commercially-available acoustic and electronic devices in varying levels of simulated noisy clinical settings and quantified using two metrics that reflect perceptual audibility and statistical similarity, normalized covariance measure (NCM) and magnitude squared coherence (MSC). The analyses highlight the major limitations of current stethoscopes and the significant improvements the proposed system makes in challenging settings by minimizing both distortion of lung sounds and contamination by ambient noise.

  DOI

• Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children

  Pediatric Pulmonology · 2020 · 22 citations

  • Medicine
  • Pediatrics
  • Internal medicine

  BACKGROUND: Whether digitally recorded lung sounds are associated with radiographic pneumonia or clinical outcomes among children in low-income and middle-income countries is unknown. We sought to address these knowledge gaps. METHODS: We enrolled 1 to 59monthold children hospitalized with pneumonia at eight African and Asian Pneumonia Etiology Research for Child Health sites in six countries, recorded digital stethoscope lung sounds, obtained chest radiographs, and collected clinical outcomes. Recordings were processed and classified into binary categories positive or negative for adventitial lung sounds. Listening and reading panels classified recordings and radiographs. Recording classification associations with chest radiographs with World Health Organization (WHO)-defined primary endpoint pneumonia (radiographic pneumonia) or mortality were evaluated. We also examined case fatality among risk strata. RESULTS: Among children without WHO danger signs, wheezing (without crackles) had a lower adjusted odds ratio (aOR) for radiographic pneumonia (0.35, 95% confidence interval (CI): 0.15, 0.82), compared to children with normal recordings. Neither crackle only (no wheeze) (aOR: 2.13, 95% CI: 0.91, 4.96) or any wheeze (with or without crackle) (aOR: 0.63, 95% CI: 0.34, 1.15) were associated with radiographic pneumonia. Among children with WHO danger signs no lung recording classification was independently associated with radiographic pneumonia, although trends toward greater odds of radiographic pneumonia were observed among children classified with crackle only (no wheeze) or any wheeze (with or without crackle). Among children without WHO danger signs, those with recorded wheezing had a lower case fatality than those without wheezing (3.8% vs. 9.1%, p = .03). CONCLUSIONS: Among lower risk children without WHO danger signs digitally recorded wheezing is associated with a lower odds for radiographic pneumonia and with lower mortality. Although further research is needed, these data indicate that with further development digital auscultation may eventually contribute to child pneumonia care.

  PublisherOA PDFDOI

• Push-pull competition between bottom-up and top-down auditory attention to natural soundscapes

  eLife · 2020 · 58 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Cognitive psychology

  In everyday social environments, demands on attentional resources dynamically shift to balance our attention to targets of interest while alerting us to important objects in our surrounds. The current study uses electroencephalography to explore how the push-pull interaction between top-down and bottom-up attention manifests itself in dynamic auditory scenes. Using natural soundscapes as distractors while subjects attend to a controlled rhythmic sound sequence, we find that salient events in background scenes significantly suppress phase-locking and gamma responses to the attended sequence, countering enhancement effects observed for attended targets. In line with a hypothesis of limited attentional resources, the modulation of neural activity by bottom-up attention is graded by degree of salience of ambient events. The study also provides insights into the interplay between endogenous and exogenous attention during natural soundscapes, with both forms of attention engaging a common fronto-parietal network at different time lags.

  DOI

Recent grants

• Multiscale modeling of the cocktail party problem

  NIH · $2.3M · 2018–2024

• Smart stethoscope for monitoring and diagnosis of lung diseases

  NIH · $2.3M · 2016–2021

• SCH: Smart Auscultation for Pulmonary Diagnostics and Imaging

  NIH · $1.1M · 2022–2026

• Feelix@Home: A smart stethoscope to improve pediatric asthma management for urban minority families

  NIH · $222k · 2019–2021

• Career: Cognitive Auditory Systems for Processing of Complex Acoustic Scenes

  NSF · $556k · 2009–2015

Frequent coauthors

• Shihab Shamma

  University of Maryland, College Park

  51 shared

• Jonathan B. Fritz

  New York University

  18 shared

• Sridhar Krishna Nemala

  14 shared

• Eric D. McCollum

  Johns Hopkins University

  13 shared

• Bernhard Englitz

  12 shared

• Joel S. Snyder

  University of Nevada, Las Vegas

  11 shared

• Sangwook Park

  SK Group (South Korea)

  10 shared

• Kailash Patil

  Vishwakarma University

  10 shared

Awards & honors

• Johns Hopkins University Catalyst Award (2017)
• Kenan Award for Innovative Projects in Undergraduate Educati…
• Office of Naval Research Young Investigator Award
• National Science Foundation Early Career Award

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