Evidence map›Paper›PMID 32764362›Full record

ArticleSensors (Basel, Switzerland)2020

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM.

Phetcharat Parathai, Naruephorn Tengtrairat, Wai Lok Woo, Mohammed Abdul Muttaleb Abdullah, Gholamreza Rafiee, Ossama Alshabrawy

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Sensor Signal and Information Processing III.Sensors (Basel, Switzerland) · 2020
    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

6 authors.

Phetcharat ParathaiSchool of Software Engineering, Payap University, Chiang Mai 50000, Thailand.
Naruephorn TengtrairatSchool of Software Engineering, Payap University, Chiang Mai 50000, Thailand.
Wai Lok WooDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Mohammed Abdul Muttaleb AbdullahComputer and Information Engineering Department, Ninevah University, Mosul 41002, Iraq.
Gholamreza RafieeSchool of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 5BN, UK.
Ossama AlshabrawyDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive

Indexed as

audio signal processingblind signal separationnonnegative matric factorizationsound event classificationsupport vector machines

Identifiers

PMID32764362
PMCPMC7472059

What OpenQuestion holds

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LicenceCC BY
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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.