Fractal Dimensions of Music and Automatic Playlist Generation: Similarity Search via MP3 Song Uploads

Abstract

We present an automated approach to music search and playlist generation based on fractal dimensions of music. We compute 372 power-law metrics per song capturing statistical proportions of musical material. Using attribute selection and principal component analysis, we have reduced these metrics to approximately 45 independent features. These have been shown to capture important aspects of music aesthetics and similarity. Through an audio-to-MIDI transcription process, users may upload MP3 songs as search queries, in real time. This new development enables construction of music recommendation systems, which may work with previously unknown music. Unlike Pandora, last.fm, and Genius, such systems will analyze the actual music (potentially like the human ear), as opposed to harvesting information from humans (e.g., websites, user preferences, or musicologist recommendations). This approach combines time-frequency and spectral processing, information retrieval and audio analysis, and music classification. We present two on-line demos, using corpora from Magnatune and 7digital.