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Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Kenya Forest Sounds Dataset

Authors: Vikiru, Allan; Simiyu, Daniel; Kahoro, Sandra; Muti, Zainabu; Kamau, Solomon; Macharia, Anthony; Zemp, Delphine Clara; +1 Authors

Kenya Forest Sounds Dataset

Abstract

Kenya Forest Sounds Dataset License: Creative Commons Attribution 4.0 International (CC BY 4.0) Authors and Contributors All authors and contributors are listed on Zenodo: https://zenodo.org/records/21443074 About The Kenya Forest Sounds Dataset contains 5,500 audio recordings collected during excursions to three forests in the Kenyan South Rift region: Oloolua Forest (Kajiado County) Ngong Hills Forest Reserve (Kajiado County) Nyangores Forest (Bomet County) The primary purpose of this dataset is to address the limited availability of natural sound recordings from tropical forest ecosystems. The dataset can be used for machine learning classification tasks that detect and classify natural sounds occurring in forest environments. Potential Use Cases Detecting human conversation in forest ecosystems. Identifying Swahili-English human conversations. Acoustic biodiversity and environmental monitoring. Training and evaluation of audio classification models. Refer to the datasheet on the Zenodo record for more details regarding collection and use of the dataset. Dataset Structure Audio files are provided in WAV format using the naming convention: ZOOM0XXX_X_16k.wav Clip durations range from 0.07 seconds to 5.02 seconds. The dataset README (Dataset_Description.xlsx) contains two annotation tabs: Human-Annotated ML-Annotated-People-Talking The Human-Annotated subset contains 3,164 sounds labelled by human annotators. The ML-Annotated-People-Talking subset contains 2,336 sounds of human conversation detected by a pre-trained model and subsequently validated by human annotators. Details of the ML annotation workflow are available at: https://github.com/SCES-Makerspace-Lab/Auto-Sound-Annotation-2026 Sound Labels The dataset contains the following labels as done conventionally by the research team: Label Count Axe 90 Bag 12 Bag Zip 22 Birds 213 car passing 1 Chainsaw 129 Child 2 cow mooing 2 Creaking 1 Crickets 3 Cutting wood 7 Distant talking 15 disturbance noise 6 Drone 5 Engine 3 Flowing water 21 Livestock 26 Machine beeping 1 Machine moving 6 Motorcycle 5 Moving object 8 Moving vehicle 6 Objects moving 46 Objects moving in water 11 passing vehicle 3 people talking 39 people talking and laughing 1 person laughing 2 Person moving 9 Person singing 3 Person talking 3448 person talking over disturbance noise 1 Person walking 379 Phone 4 Plastic wrapper 11 pouring water 4 radio 1 Shuffling 13 sloshing water 4 sloshing water and talking 1 Steel banging 1 Vehicle 703 White Noise 1 Wind 231 SUM 5500 Citation and Attribution When using this dataset, please cite the dataset record available on Zenodo: https://zenodo.org/records/21443074 Any questions/issues are more than welcome: please write to Allan Vikiru (avikiru@strathmore.edu) with your query!

Keywords

Sound, Tropical forest, Environmental Sounds, Forest conservation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Related to Research communities
Italian National Biodiversity Future Center