Query-by-Dancing: A Dance Music Retrieval System Based on Body-Motion Similarity

Tsuchida, Shuhei; Fukayama, Satoru; Goto, Masataka

doi:10.1007/978-3-030-05710-7_21

Cited by 8 publications

(11 citation statements)

References 8 publications

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Section: Music To Motion Generationmentioning

confidence: 99%

“…On the contrary, there are very few work on automatic music generation or music recommendation according to human motions [41], [45]. Tsuchida et al [41] propose a music recommendation method according to the dance motions. The method calculates the similarity between the query motion and the motion trajectory in the database, but the motion data are in the form of RGB images and only the 2dimensional coordinate points are considered.…”

Section: Music To Motion Generationmentioning

confidence: 99%

“…Previous music retrieval and recommendation methods only depend on audio features and metadata [60]. There are very few related works on cross-media solutions, such as music recommendation based on dance motions [41], [45]. Ohkushi et al [45] propose to model the correlation between the dance video and the music based on kernel canonical correlation analysis (CCA), and recommend music pieces based on a query video sequence.…”

Section: Music Retrieval and Recommendationmentioning

confidence: 99%

“…Ohkushi et al [45] propose to model the correlation between the dance video and the music based on kernel canonical correlation analysis (CCA), and recommend music pieces based on a query video sequence. Tsuchida et al [41] utilize human poses predicted from video sequences as features to calculate similarities among dance video, and predict dance music given input dance videos.…”

Section: Music Retrieval and Recommendationmentioning

confidence: 99%

“…Music recommendation based on dance motions can be applied to automatic video editing such as automatic background music selection, and automatic music composition, etc. Despite its broad applications, there are only a few studies done on this topic [41], [45]. This work investigates music recommendation method based on dance motions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Deep Music Recommendation Method Based on Human Motion Analysis

Gong

2021

IEEE Access

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Despite its applications in automatic video editing and automatic music composition, the problem of music recommendation from dance motions has seldom been explored. In order to solve this problem, this work proposes a deep music recommendation algorithm based on dance motion analysis and evaluate it through quantitative measures. For quantitative evaluation, this work implements a LSTM-AE based music recommendation method which learns the correspondences between motion and music. In experiments, the two methodologies are compared and the motion analysis based methods outperform their rival by large margins. This work also proposes a quantitative measure of accurately recommended music genre. The proposed motion analysis based method achieves a recommendation accuracy of 91.3% using late fusion of joint and limb features.

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Section: Music To Motion Generationmentioning

confidence: 99%

Section: Music To Motion Generationmentioning

confidence: 99%

Section: Music Retrieval and Recommendationmentioning

confidence: 99%

Section: Music Retrieval and Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Deep Music Recommendation Method Based on Human Motion Analysis

Gong

2021

IEEE Access

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The Possibility of Personality Extraction Using Skeletal Information in Hip-Hop Dance by Human or Machine

Furuichi

Abe

Nakamura

2019

Human-Computer Interaction – INTERACT 2019

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The same dance can give different impressions depending on the way the dancers convey their own emotions and personality through their interpretation of the dance. Beginner dancers who are teaching themselves often search for dance videos online that match their own personality in order to practice and mimic them, but it is not easy to find a dance that suits their own personality and skill level. In this work, we examined hip-hop dance to determine whether it is possible to identify one's own dance from skeleton information acquired by Kinect and whether it is possible to mechanically extract information representing the individuality of dance. Experimental results showed that rich experienced dancers could distinguish their own dances by only skeleton information, and it was also possible to distinguish from averaged skeletal information. Furthermore, we generated features from the skeletal information of dance and clarified that individual dance can be distinguished accurately by machine learning.

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Body Data for Music Information Retrieval Tasks

Kelkar¹

2021

Recommender Systems for Medicine and Music

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Query-by-Dancing: A Dance Music Retrieval System Based on Body-Motion Similarity

Cited by 8 publications

References 8 publications

A Deep Music Recommendation Method Based on Human Motion Analysis

A Deep Music Recommendation Method Based on Human Motion Analysis

The Possibility of Personality Extraction Using Skeletal Information in Hip-Hop Dance by Human or Machine

Body Data for Music Information Retrieval Tasks

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