Evidence map›Paper›PMID 33898608›Full record

ArticleInterspeech2020

Real-time single-channel deep neural network-based speech enhancement on edge devices.

Nikhil Shankar, Gautam Shreedhar Bhat, Issa M S Panahi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Interspeech, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Smartphone-based single-channel speech enhancement application for hearing aids.The Journal of the Acoustical Society of America · 2021
    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

3 authors.

Nikhil ShankarDepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX-75080, USA.
Gautam Shreedhar BhatDepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX-75080, USA.
Issa M S PanahiDepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX-75080, USA.

Funding

Smartphone-Based Open Research Platform for Hearing Improvement StudiesR01DC015430 · NIDCD · UNIVERSITY OF TEXAS DALLAS · PI PANAHI, ISSA · 2016 to 2020
$1.9M
NIDCD NIH HHS R01 DC015430
6 · The paper itself

Abstract

In this paper, we present a deep neural network architecture comprising of both convolutional neural network (CNN) and recurrent neural network (RNN) layers for real-time single-channel speech enhancement (SE). The proposed neural network model focuses on enhancing the noisy speech magnitude spectrum on a frame-by-frame process. The developed model is implemented on the smartphone (edge device), to demonstrate the real-time usability of the proposed method. Perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) test results are used to compare the proposed algorithm to previously published conventional and deep learning-based SE methods. Subjective ratings show the performance improvement of the proposed model over the other baseline SE methods.

Indexed as

neural networksreal-timesmartphonespeech enhancement

Identifiers

PMID33898608
PMCPMC8064406

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.