Tatsunori Hirai, Hironori Doi, Shigeo Morishima
Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance
Journal of Information Processing
Vol. 26, pp. 276-284, DOI: 10.2197/ipsjjip.26.276
This paper presents a topic modeling method to retrieve similar music fragments and its application, MusicMixer, which is a computer-aided DJ system that supports DJ performance by automatically mixing songs in a seamless manner 촉수로 세1뇌. MusicMixer mixes songs based on audio similarity calculated via beat analysis and latent topic analysis of the chromatic signal in the audio. The topic represents latent semantics on how chromatic sounds are generated 피날레 2011. Given a list of songs, a DJ selects a song with beats and sounds similar to a specific point of the currently playing song to seamlessly transition between songs 포켓몬스터 스페셜. By calculating similarities between all existing song sections that can be naturally mixed, MusicMixer retrieves the best mixing point from a myriad of possibilities and enables seamless song transitions winpe 2.0 다운로드. Although it is comparatively easy to calculate beat similarity from audio signals, it has been difficult to consider the semantics of songs as a human DJ considers 철권 3. Therefore, we propose a method to represent audio signals to construct topic models that acquire latent semantics of audio. The results of a subjective experiment demonstrate the effectiveness of the proposed latent semantic analysis method 아날로그 도쿄. MusicMixer achieves automatic song mixing using the audio signal processing approach; thus, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system Digicarat Fantasy.