TY - GEN
T1 - MusicMixer
T2 - 12th Advances in Computer Entertainment Technology Conference, ACE 2015
AU - Hirai, Tatsunori
AU - Doi, Hironori
AU - Morishima, Shigeo
N1 - Funding Information:
This work was supported by OngaCREST, CREST, JST and JSPS Grant-in-Aid for JSPS Fellows. This work was inspired by Tonkatsu DJ Agetaro.
PY - 2015/11/16
Y1 - 2015/11/16
N2 - In this paper, we present MusicMixer, a computer-aided DJ system that helps DJs, specifically with song mixing. Mu-sicMixer continuously mixes and plays songs using an automatic music mixing method that employs audio similarity calculations. By calculating similarities between song sections that can be naturally mixed, MusicMixer enables seamless song transitions. Though song mixing is the most fundamental and important factor in DJ performance, it is difficult for untrained people to seamlessly connect songs. Mu-sicMixer realizes automatic song mixing using an audio signal processing approach; therefore, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system, enabling effective DJ song mixing and lowering entry barriers for the inexperienced. We also propose personalization for song suggestions using a preference memorization function of MusicMixer.
AB - In this paper, we present MusicMixer, a computer-aided DJ system that helps DJs, specifically with song mixing. Mu-sicMixer continuously mixes and plays songs using an automatic music mixing method that employs audio similarity calculations. By calculating similarities between song sections that can be naturally mixed, MusicMixer enables seamless song transitions. Though song mixing is the most fundamental and important factor in DJ performance, it is difficult for untrained people to seamlessly connect songs. Mu-sicMixer realizes automatic song mixing using an audio signal processing approach; therefore, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system, enabling effective DJ song mixing and lowering entry barriers for the inexperienced. We also propose personalization for song suggestions using a preference memorization function of MusicMixer.
KW - Automatic song mixing
KW - Computer-aided DJ system
KW - Human-content interaction
UR - http://www.scopus.com/inward/record.url?scp=84979742088&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979742088&partnerID=8YFLogxK
U2 - 10.1145/2832932.2832942
DO - 10.1145/2832932.2832942
M3 - Conference contribution
AN - SCOPUS:84979742088
T3 - ACM International Conference Proceeding Series
BT - ACE 2015 - 12th Advances in Computer Entertainment Technology Conference, Proceedings
PB - Association for Computing Machinery
Y2 - 16 November 2015 through 19 November 2015
ER -