Music, Mind and Machine

The audio bitstream in music encodes a high amount of statistical, acoustic, emotional and cultural information. But music also has an important linguistic accessory; most musical artists are described in great detail in record reviews, fan sites and news items. We high-light current and ongoing research into extract-ing relevant features from audio and simulta-neously learning language features linked to the music. We show results in a “query-by-description ” task in which we learn the per-ceptual meaning of automatically-discovered single-term descriptive components, as well as a method of automatically uncovering ‘seman-tically attached ’ terms (terms that have percep-tual grounding.) We then show recent work in ‘semantic basis functions ’ – parameter spaces of description (such as fast... slow or male... female) that encode the highest descriptive variance in a semantic space.