{"title":"基于音乐特征集群和动态编程的音乐驱动编舞","authors":"Shuhong Lin;Moshe Zukerman;Hong Yan","doi":"10.1109/TMM.2024.3390232","DOIUrl":null,"url":null,"abstract":"Generating choreography from music poses a significant challenge. Conventional dance generation methods are limited by only being able to match specific dance movements to music with corresponding rhythms, restricting the utilization of existing dance sequences. To address this limitation, we propose a method that generates a label, based on a probability distribution function derived from music features, that can be applied to music segments of varying lengths. By using the Kullback-Leibler divergence, we assess the similarity between music segments based on these labels. To ensure adaptability to different musical rhythms, we employ a cubic spline method to represent dance movements. This approach allows us to control the speed of a dance sequence by resampling it, enabling adaptation to varying rhythms based on the tempo of newly input music. To evaluate the effectiveness of our method, we compared the dances generated by our approach with those generated by other neural network-based and conventional methods. Quantitative evaluations demonstrated that our method outperforms these alternatives in terms of dance quality and fidelity.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":"26 ","pages":"9330-9341"},"PeriodicalIF":8.4000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Music-Driven Choreography Based on Music Feature Clusters and Dynamic Programming\",\"authors\":\"Shuhong Lin;Moshe Zukerman;Hong Yan\",\"doi\":\"10.1109/TMM.2024.3390232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generating choreography from music poses a significant challenge. Conventional dance generation methods are limited by only being able to match specific dance movements to music with corresponding rhythms, restricting the utilization of existing dance sequences. To address this limitation, we propose a method that generates a label, based on a probability distribution function derived from music features, that can be applied to music segments of varying lengths. By using the Kullback-Leibler divergence, we assess the similarity between music segments based on these labels. To ensure adaptability to different musical rhythms, we employ a cubic spline method to represent dance movements. This approach allows us to control the speed of a dance sequence by resampling it, enabling adaptation to varying rhythms based on the tempo of newly input music. To evaluate the effectiveness of our method, we compared the dances generated by our approach with those generated by other neural network-based and conventional methods. Quantitative evaluations demonstrated that our method outperforms these alternatives in terms of dance quality and fidelity.\",\"PeriodicalId\":13273,\"journal\":{\"name\":\"IEEE Transactions on Multimedia\",\"volume\":\"26 \",\"pages\":\"9330-9341\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Multimedia\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10504692/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Multimedia","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10504692/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Music-Driven Choreography Based on Music Feature Clusters and Dynamic Programming
Generating choreography from music poses a significant challenge. Conventional dance generation methods are limited by only being able to match specific dance movements to music with corresponding rhythms, restricting the utilization of existing dance sequences. To address this limitation, we propose a method that generates a label, based on a probability distribution function derived from music features, that can be applied to music segments of varying lengths. By using the Kullback-Leibler divergence, we assess the similarity between music segments based on these labels. To ensure adaptability to different musical rhythms, we employ a cubic spline method to represent dance movements. This approach allows us to control the speed of a dance sequence by resampling it, enabling adaptation to varying rhythms based on the tempo of newly input music. To evaluate the effectiveness of our method, we compared the dances generated by our approach with those generated by other neural network-based and conventional methods. Quantitative evaluations demonstrated that our method outperforms these alternatives in terms of dance quality and fidelity.
期刊介绍:
The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.