"Tasting" sounds by A.I. Sommelier - Unsupervised machine learning of wine evaluation applied for sound quality evaluation

Wine produced countries in European Union categorize quality of wine as three classes by subjective judgements (called as "Appellation" in French) like taste, land of producer, type of grape seed and so on. In order to evaluate those wine quality, unsupervised machine learning with measured optical and chemical data of wine enables to categorize those 3 subjective classes at high accuracies. With those "A.I. Sommelier" techniques, vacuum cleaner sounds are categorized with psychoacoustic parameters and then modeling by image recognition AI of sFFT images correlate with annoyance rating at high rate. Although unsupervised machine learning technique is uncommon, it shows unsupervised machine learning technique is useful for sound quality evaluation

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Publication Details

Journal
NOISE-CON proceedings
Published
2024-10-04
DOI
https://doi.org/10.3397/in_2024_3295
Primary Topic
Food Supply Chain Traceability
Type
article
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article

"Tasting" sounds by A.I. Sommelier - Unsupervised machine learning of wine evaluation applied for sound quality evaluation

Serkan Atamer, Norio Kubo
NOISE-CON proceedings
Food Supply Chain Traceability
article

"Tasting" sounds by A.I. Sommelier - Unsupervised machine learning of wine evaluation applied for sound quality evaluation

Serkan Atamer, Norio Kubo
article en

Abstract

Wine produced countries in European Union categorize quality of wine as three classes by subjective judgements (called as "Appellation" in French) like taste, land of producer, type of grape seed and so on. In order to evaluate those wine quality, unsupervised machine learning with measured optical and chemical data of wine enables to categorize those 3 subjective classes at high accuracies. With those "A.I. Sommelier" techniques, vacuum cleaner sounds are categorized with psychoacoustic parameters and then modeling by image recognition AI of sFFT images correlate with annoyance rating at high rate. Although unsupervised machine learning technique is uncommon, it shows unsupervised machine learning technique is useful for sound quality evaluation

NOISE-CON proceedingsVol. 270(8)
Technische Universität Dresden (DE)
Responsible consumption and production
Openalex Percentile: Top 79%
Food Supply Chain Traceability
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