Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer
Abstract Objective digital measurement of gamblers visiting gambling venues is conducted using cashless cards and facial recognition systems, but these methods are confined within a single gambling venue. Hence, we propose an objective digital measurement method using a transformer, a state-of-the-a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83389-1 |
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| author | Kenji Yokotani Tetsuya Yamamoto Hideyuki Takahashi Masahiro Takamura Nobuhito Abe |
| author_facet | Kenji Yokotani Tetsuya Yamamoto Hideyuki Takahashi Masahiro Takamura Nobuhito Abe |
| author_sort | Kenji Yokotani |
| collection | DOAJ |
| description | Abstract Objective digital measurement of gamblers visiting gambling venues is conducted using cashless cards and facial recognition systems, but these methods are confined within a single gambling venue. Hence, we propose an objective digital measurement method using a transformer, a state-of-the-art machine learning approach, to detect total gambling venue visitations for gamblers who visit multiple gambling venues using sounds in gamblers’ environments. We sampled gambling and nongambling event datasets from websites to create a gambling play classifier. We also sampled gambling and nongambling location datasets for a gambling location detector. Further, we sampled practical dataset with four different recording conditions and two different recording devices. Our Swin transformer model with 54 classes (4 gambling play classes and 50 nongambling event classes) achieved highest accuracy (0.801). The gambling location detector of the Swin transformer also achieved high performance; the areas under the receiver operating characteristic curves (AUCs) for bingo, mahjong, pachinko, and electronic gambling machine plays were 0.845, 0.780, 0.826, and 0.833, respectively. Moreover, gambling visitation detector of the Swin transformer showed high performance especially in Pachinko (AUCs 0.972–0.715) regardless of their recording conditions and devices. These preliminary findings highlight the potential of environmental sounds to detect visits to gambling venues. |
| format | Article |
| id | doaj-art-2c66181a81a7445baa0e91aac58577c1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2c66181a81a7445baa0e91aac58577c12025-01-05T12:13:19ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-83389-1Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformerKenji Yokotani0Tetsuya Yamamoto1Hideyuki Takahashi2Masahiro Takamura3Nobuhito Abe4Graduate School of Technology, Industrial and Social Sciences, Tokushima UniversityGraduate School of Technology, Industrial and Social Sciences, Tokushima UniversityGraduate School of Engineering Science, Osaka UniversityInstitutional Research Center, Fujita Health UniversityInstitute for the Future of Human Society, Kyoto UniversityAbstract Objective digital measurement of gamblers visiting gambling venues is conducted using cashless cards and facial recognition systems, but these methods are confined within a single gambling venue. Hence, we propose an objective digital measurement method using a transformer, a state-of-the-art machine learning approach, to detect total gambling venue visitations for gamblers who visit multiple gambling venues using sounds in gamblers’ environments. We sampled gambling and nongambling event datasets from websites to create a gambling play classifier. We also sampled gambling and nongambling location datasets for a gambling location detector. Further, we sampled practical dataset with four different recording conditions and two different recording devices. Our Swin transformer model with 54 classes (4 gambling play classes and 50 nongambling event classes) achieved highest accuracy (0.801). The gambling location detector of the Swin transformer also achieved high performance; the areas under the receiver operating characteristic curves (AUCs) for bingo, mahjong, pachinko, and electronic gambling machine plays were 0.845, 0.780, 0.826, and 0.833, respectively. Moreover, gambling visitation detector of the Swin transformer showed high performance especially in Pachinko (AUCs 0.972–0.715) regardless of their recording conditions and devices. These preliminary findings highlight the potential of environmental sounds to detect visits to gambling venues.https://doi.org/10.1038/s41598-024-83389-1Gambling venue visitationAcoustic featuresEnvironmental soundsSwin transformerDigital markerElectronic gambling machine |
| spellingShingle | Kenji Yokotani Tetsuya Yamamoto Hideyuki Takahashi Masahiro Takamura Nobuhito Abe Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer Scientific Reports Gambling venue visitation Acoustic features Environmental sounds Swin transformer Digital marker Electronic gambling machine |
| title | Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer |
| title_full | Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer |
| title_fullStr | Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer |
| title_full_unstemmed | Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer |
| title_short | Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer |
| title_sort | sounds like gambling detection of gambling venue visitation from sounds in gamblers environments using a transformer |
| topic | Gambling venue visitation Acoustic features Environmental sounds Swin transformer Digital marker Electronic gambling machine |
| url | https://doi.org/10.1038/s41598-024-83389-1 |
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