Abstract
This paper presents MusicMixer, an automatic DJ system that mixes songs in a seamless manner. 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 about how chromatic sounds are generated. Given a list of songs, a DJ selects a song with beat and sounds similar to a specific point of the currently playing song to seamlessly transition between songs. By calculating the similarity of all existing pairs of songs, the proposed system can retrieve the best mixing point from innumerable possibilities. 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. To consider such semantics, 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.
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Notes
- 1.
The word “mix” here refers to the gradual transiton of one song to another.
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Acknowledgments
This work was supported by OngaCREST, CREST, JST and JSPS Grant-in-Aid for JSPS Fellows. This work was inspired by Tonkatsu DJ Agetaro.
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Hirai, T., Doi, H., Morishima, S. (2016). MusicMixer: Automatic DJ System Considering Beat and Latent Topic Similarity. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_59
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DOI: https://doi.org/10.1007/978-3-319-27671-7_59
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