Annotated dataset of simulated voiding sound for urine flow estimation

Sound-based uroflowmetry is a non-invasive test emerging as an alternative to standard uroflowmetry, estimating voiding characteristics from the sound generated by urine striking water in a toilet bowl. The lack of labeled flow sound datasets limits research for developing supervised AI algorithms....

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Bibliographic Details
Published in:Scientific data Vol. 12; no. 1; pp. 993 - 7
Main Authors: Alvarez, Marcos Lazaro, Arjona, Laura, Bahillo, Alfonso, Bernardo-Seisdedos, Ganeko
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 13.06.2025
Nature Publishing Group
Nature Portfolio
Subjects:
692/308/1426
692/699/2768/2767
692/700/784
Algorithms
Artificial intelligence
Body height
Data Descriptor
Datasets
Humanities and Social Sciences
Humans
Microphones
multidisciplinary
Science
Science (multidisciplinary)
Smartphones
Sound
Urethra
Urination
Urine
Urodynamics
ISSN:2052-4463, 2052-4463
Online Access:Get full text
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Summary:Sound-based uroflowmetry is a non-invasive test emerging as an alternative to standard uroflowmetry, estimating voiding characteristics from the sound generated by urine striking water in a toilet bowl. The lack of labeled flow sound datasets limits research for developing supervised AI algorithms. This work presents a dataset of simulated urinary flow sound recordings at flow rates from 1 to 50 ml/s, in increments of 1 ml/s, against water in a real toilet bowl. Flow generation employed an L600-1F precision peristaltic pump, with simultaneous recordings from three devices: high-quality Ultramic384k microphone, Mi A1 smartphone and Oppo smartwatch. Water was expelled through a 6 mm diameter nozzle (simulating the urethra) from a variable height of 73 to 86 cm, mimicking adult urination. The dataset provides 60-seconds labeled, constant-flow audio recordings (WAV format). This resource is intended to support research on sound-based urinary flow estimation by developing and validating supervised artificial intelligence algorithms.
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ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-025-05358-1