Evidence map›Paper›PMID 31379421›Full record

ArticleConference record. Asilomar Conference on Signals, Systems & Computers2018

A Wearable Platform for Research in Augmented Hearing.

Louis Pisha, Sean Hamilton, Dhiman Sengupta, Ching-Hua Lee, Krishna Chaithanya Vastare, Tamara Zubatiy, Sergio Luna, Cagri Yalcin, Alex Grant, Rajesh Gupta and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Conference record. Asilomar Conference on Signals, Systems & Computers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. A Wearable, Extensible, Open-Source Platform for Hearing Healthcare Research.IEEE access : practical innovations, open solutions · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 1 institution in 1 country.

Louis PishaDepartment of Electrical and Computer Engineering, University of California, San Diego.
Sean HamiltonDepartment of Computer Science and Engineering, University of California, San Diego.
Dhiman SenguptaDepartment of Computer Science and Engineering, University of California, San Diego.
Ching-Hua LeeDepartment of Electrical and Computer Engineering, University of California, San Diego.
Krishna Chaithanya VastareDepartment of Electrical and Computer Engineering, University of California, San Diego.
Tamara ZubatiyCalifornia Institute for Telecommunications and Information Technology (Calit2), University of California, San Diego.
Sergio LunaDepartment of Mathematics, University of California, San Diego.
Cagri YalcinCalifornia Institute for Telecommunications and Information Technology (Calit2), University of California, San Diego.
Alex GrantCalifornia Institute for Telecommunications and Information Technology (Calit2), University of California, San Diego.
Rajesh GuptaDepartment of Computer Science and Engineering, University of California, San Diego.
Ganz ChockalingamCalifornia Institute for Telecommunications and Information Technology (Calit2), University of California, San Diego.
Bhaskar D RaoDepartment of Electrical and Computer Engineering, University of California, San Diego.
Harinath GarudadriDepartment of Electrical and Computer Engineering, University of California, San Diego.
University of California, San Diego · US

Funding

Quantifying Physical and Social Isolation, and Assessing Perceived LonelinessR01DC015436 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GARUDADRI, HARINATH · 2016 to 2020
$2.4M
Self fitting of Amplification: Methodology and CandidacyR33DC015046 · NIDCD · SAN DIEGO STATE UNIVERSITY · PI GARUDADRI, HARINATH, MACKERSIE, CAROL L · 2017 to 2020
$2.0M
NIDCD NIH HHS R01 DC015436NIDCD NIH HHS R33 DC015046
6 · The paper itself

Abstract

We have previously reported a realtime, open-source speech-processing platform (OSP) for hearing aids (HAs) research. In this contribution, we describe a wearable version of this platform to facilitate audiological studies in the lab and in the field. The system is based on smartphone chipsets to leverage power efficiency in terms of FLOPS/watt and economies of scale. We present the system architecture and discuss salient design elements in support of HA research. The ear-level assemblies support up to 4 microphones on each ear, with 96 kHz, 24 bit codecs. The wearable unit runs OSP Release 2018c on top of 64-bit Debian Linux for binaural HA with an overall latency of 5.6 ms. The wearable unit also hosts an embedded web server (EWS) to monitor and control the HA state in realtime. We describe three example web apps in support of typical audiological studies they enable. Finally, we describe a baseline speech enhancement module included with Release 2018c, and describe extensions to the algorithms as future work.

Indexed as

hearablesHearing aidsOpen Speech Platform (OSP)speech and audio processing

Identifiers

PMID31379421
PMCPMC6677400
OpenAlexW2919310412

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.