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A Wearable Platform for Research in Augmented Hearing.

Louis Pisha1, Sean Hamilton2, Dhiman Sengupta2

  • 1Department of Electrical and Computer Engineering, University of California, San Diego.

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|August 6, 2019
PubMed
Summary
This summary is machine-generated.

A new wearable platform for hearing aid (HA) research offers real-time speech processing. This system uses smartphone chipsets for efficiency, enabling advanced audiological studies in diverse settings.

Keywords:
Hearing aidsOpen Speech Platform (OSP)hearablesspeech and audio processing

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Area of Science:

  • Audiology
  • Signal Processing
  • Wearable Technology

Background:

  • Previous work established an open-source speech-processing platform (OSP) for hearing aid (HA) research.
  • Advancements in wearable technology and smartphone chipsets enable more portable and efficient research tools.

Purpose of the Study:

  • To introduce a wearable version of the OSP for enhanced audiological studies.
  • To leverage smartphone chipsets for power efficiency and scalability in HA research.

Main Methods:

  • Developed a wearable system using smartphone chipsets with ear-level assemblies supporting 4 microphones per ear (96 kHz, 24 bit).
  • The system runs OSP Release 2018c on 64-bit Debian Linux, achieving a binaural HA latency of 5.6 ms.
  • Integrated an embedded web server (EWS) for real-time monitoring and control, with example web apps for audiological studies.

Main Results:

  • Demonstrated a wearable OSP system facilitating lab and field audiological studies.
  • Achieved low latency (5.6 ms) for binaural hearing aid processing.
  • Included a baseline speech enhancement module with potential for future algorithmic extensions.

Conclusions:

  • The wearable OSP platform enhances the feasibility of audiological research both in the lab and in real-world settings.
  • The system's design, based on efficient smartphone chipsets, supports scalable and power-effective hearing aid research.
  • Future work will focus on extending the speech enhancement algorithms for improved audiological applications.