Research Article
Dance Step Recommender
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 175 - Issue 15 |
| Published: Aug 2020 |
| Authors: Chinmay Kulkarni, Ritesh Nemade, Sanket Sutar, Akshay Tarde, Mandar Deshpande, Madhuri Joshi |
10.5120/ijca2020920647
|
Chinmay Kulkarni, Ritesh Nemade, Sanket Sutar, Akshay Tarde, Mandar Deshpande, Madhuri Joshi . Dance Step Recommender. International Journal of Computer Applications. 175, 15 (Aug 2020), 26-29. DOI=10.5120/ijca2020920647
@article{ 10.5120/ijca2020920647,
author = { Chinmay Kulkarni,Ritesh Nemade,Sanket Sutar,Akshay Tarde,Mandar Deshpande,Madhuri Joshi },
title = { Dance Step Recommender },
journal = { International Journal of Computer Applications },
year = { 2020 },
volume = { 175 },
number = { 15 },
pages = { 26-29 },
doi = { 10.5120/ijca2020920647 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2020
%A Chinmay Kulkarni
%A Ritesh Nemade
%A Sanket Sutar
%A Akshay Tarde
%A Mandar Deshpande
%A Madhuri Joshi
%T Dance Step Recommender%T
%J International Journal of Computer Applications
%V 175
%N 15
%P 26-29
%R 10.5120/ijca2020920647
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The huge measure of information accessible on the Internet has prompted the improvement of clever frameworks. Lately, there has been a ton of consideration in frameworks like music recommender, book recommender, film recommender, and so on. Likewise, the motivation behind this project is to use soft computing techniques to develop a choreography system. This article outlines a detailed summary of the “Dance Choreography System”. The system choreographs dance by recommending dance steps, considering the time signature and tempo of the given soundtrack as its core components. The paper incorporates a portrayal of the subject, framework engineering, and gives an itemized depiction of the work done to point. The project outputs the dance steps, applied to an animated character that is displayed on the browser.
References
- Mikel Gainza, Eugene Coyle, Vienna, Austria, 2007. Time Signature Detection by Using a Multi-Resolution Audio Similarity Matrix
- Spotify Audio Features: https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/
- Spotify Audio Analysis: https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-analysis/
- Mixamo 3D Animations: https://www.mixamo.com/#/?genres=Dance&page=1&type=Motion%2CMotionPack
- Jos Dirksen, Learn Three.js - Third Edition, 2018, Packt Publishing Ltd
- Jos Dirksen, Three.js Essentials, 2014, Packt Publishing Ltd
- Douglas W. Oard and Jinmook Kim, AAAI 1998, Implicit Feedback for Recommender Systems
- Ladislav Peska, 2016, Using the Context of User Feedback in Recommender Systems
- Ubisoft Just Dance: https://www.ubisoft.com/en-us/game/just-dance-2020
- Stefano Corazza, 2011, Real-Time Automatic Concatenation Of 3D Animation Sequences
- Shani G, Gunawardana A (2009) Evaluating recommender systems. Microsoft Research.
- Zhou T, Kuscsik Z, Liu J et al (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc Natl Acad Sci 107(10):4511–4515
- Zhang Y, Séaghdha D, Quercia D, Jambor T (2012) Auralist: introducing serendipity into music recommendation. In: WSDM’12. ACM, New York, NY, USA, pp 13–22
Index Terms
Keywords