Evidence map›Paper›PMID 42007083›Full record

ArticleData in brief2026

GESMA: A dataset of Ghanaian environmental soundscapes for machine learning applications.

Rose-Mary Owusuaa Mensah Gyening, Juliet Arthur, Prince Dawson Tetteh, Felix Adu Mensah Amofah, Kelvin Ackah, Enoch Acheampong, Betty Agyei Kponyo, Justice Owusu Agyemang, Emmanuel Ahene, Vida Kasore and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2026. 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. 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

11 authors.

Rose-Mary Owusuaa Mensah GyeningResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Juliet ArthurResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Prince Dawson TettehResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Felix Adu Mensah AmofahResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Kelvin AckahResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Enoch AcheampongResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Betty Agyei KponyoResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Justice Owusu AgyemangResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Emmanuel AheneResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Vida KasoreResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.
Jerry John KponyoResponsible Artificial Intelligence Lab Kwame Nkrumah University of Science and Technology Kumasi, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a dataset of real-world Ghanaian environmental soundscapes intended to support machine-listening research and sound-event classification in low-resource contexts. The collection contains 22,193 uncompressed 44.1 kHz/16-bit WAV recordings, captured using mobile devices across diverse environments, including urban spaces, educational institutions, marketplaces, transport hubs, and human non-verbal acoustic settings. Recordings were obtained under natural field conditions to retain authentic background noise, reverberation, and overlapping sound events. Each file is accompanied by structured metadata specifying category, class, subclass, location, and context, and all annotations have been manually verified to ensure label consistency and quality. The dataset addresses a critical geographic gap in global audio resources and provides culturally and acoustically representative material from Sub-Saharan Africa. It offers strong potential for applications in environmental monitoring, sound event detection, accessibility tools, hearing-assistive technologies, and broader audio-based AI systems.

Indexed as

Acoustic scene analysisAfrican audio datasetsContext-aware machine listeningEnvironmental soundscapesSound event classification

Identifiers

PMID42007083
PMCPMC13090659

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.