Hannah Gao, Christopher Williams, V. G. R. Varela, Changzhi Li
{"title":"Violin Gesture Recognition Using FMCW Radars","authors":"Hannah Gao, Christopher Williams, V. G. R. Varela, Changzhi Li","doi":"10.1109/WiSNeT56959.2023.10046213","DOIUrl":null,"url":null,"abstract":"Bowing gestures are a key component of violin playing and can be analyzed to provide feedback on a violinist's performance. Radar systems have increasingly been used to recognize human movements but not yet in a musical context. In this study, a portable frequency-modulated continuous-wave (FMCW) radar is used to detect various violin bowing gestures. Range profiles and time-Doppler spectrograms are extracted from the raw signal data, and their unique characteristics allow for the differentiation of different bowing techniques and the recognition of incorrect bowing motions. The results of this study demonstrate the potential of radars in aiding musical instrument training.","PeriodicalId":186233,"journal":{"name":"2023 IEEE Topical Conference on Wireless Sensors and Sensor Networks","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Topical Conference on Wireless Sensors and Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WiSNeT56959.2023.10046213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Bowing gestures are a key component of violin playing and can be analyzed to provide feedback on a violinist's performance. Radar systems have increasingly been used to recognize human movements but not yet in a musical context. In this study, a portable frequency-modulated continuous-wave (FMCW) radar is used to detect various violin bowing gestures. Range profiles and time-Doppler spectrograms are extracted from the raw signal data, and their unique characteristics allow for the differentiation of different bowing techniques and the recognition of incorrect bowing motions. The results of this study demonstrate the potential of radars in aiding musical instrument training.