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 인젝터. 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 예쁜 글꼴. 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 Chinese dictionary. 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 새나루. 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 위치 걸. 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 Download the Wacom ctl 4100 program. 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 그들이사는세상 다운로드.