Evidence map›Paper›PMID 39001130›Full record

ArticleSensors (Basel, Switzerland)2024

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

Jian-Hong Wang, Phuong Thi Le, Weng-Sheng Bee, Wenny Ramadha Putri, Ming-Hsiang Su, Kuo-Chen Li, Shih-Lun Chen, Ji-Long He, Tuan Pham, Yung-Hui Li and 1 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Jian-Hong WangSchool of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China.ORCID 0000-0001-7396-7042
Phuong Thi LeDepartment of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan.ORCID 0000-0002-2934-4937
Weng-Sheng BeeDepartment of Computer Science and Information Engineering, National Central University, Taoyuan City 320314, Taiwan.
Wenny Ramadha PutriDepartment of Computer Science and Information Engineering, National Central University, Taoyuan City 320314, Taiwan.ORCID 0000-0002-5456-160X
Ming-Hsiang SuDepartment of Data Science, Soochow University, Taipei City 10048, Taiwan.ORCID 0000-0003-0633-774X
Kuo-Chen LiDepartment of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan.ORCID 0000-0002-0110-5491
Shih-Lun ChenDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan.ORCID 0000-0002-4079-9350
Ji-Long HeSchool of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China.
Tuan PhamFaculty of Digital Technology, University of Technology and Education-University of Đà Nẵng, Danang 550000, Vietnam.ORCID 0000-0002-9225-0191
Yung-Hui LiAI Research Center, Hon Hai Research Institute, New Taipei City 207236, Taiwan.ORCID 0000-0002-0475-3689
Jia-Ching WangDepartment of Computer Science and Information Engineering, National Central University, Taoyuan City 320314, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.

Indexed as

AlgorithmsWearable Electronic DevicesHumansSignal Processing, Computer-AssistedSound Localizationembedded systemshybrid sound source separationposition detectionsignal-to-interference ratio (SIR)speech recognition

Identifiers

PMID39001130
PMCPMC11244233

What OpenQuestion holds

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Registered trials

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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.