Armonique: Experiments in Content-Based Similarity Retrieval using Power-Law Melodic and Timbre Metrics

This paper presents results from an on-going MIR study utilizing hundreds of melodic and timbre features based on power laws for content-based similarity retrieval. These metrics are incorporated into a music search engine prototype, called Armonique. This prototype is used with a corpus of 9153 songs encoded in both MIDI and MP3 to identify pieces similar to and dissimilar from selected songs. The MIDI format is used to extract various powerlaw features measuring proportions of music-theoretic and other attributes, such as pitch, duration, melodic intervals, and chords. The MP3 format is used to extract power-law features measuring proportions within FFT power spectra related to timbre. Several assessment experiments have been conducted to evaluate the effectiveness of the similarity model. The results suggest that power-law metrics are very promising for content-based music querying and retrieval, as they seem to correlate with aspects of human emotion and aesthetics.