Abstract: Tuneprint is a software system for content-based identification of audio recordings. Tuneprint transforms its input using a perceptual model of the human auditory system, making its output robust to lossy compression and to other distortions. In order to make use both of the instantaneous pattern of a recording's perceptual features and the information contained in the evolution of these features over time, Tuneprint first matches fragments of the input against a database of fragments of known recordings. In a subsequent step, these matches at the fragment level are assembled in order to identify a single recording that matches consistently over time. In a small-scale test, Tuneprint has successfully matched all queries against a database of 100,000 commercially released recordings.

Profound thanks are due to our colleagues Daren Gill, Martin Stiaszny, Amittai Axelrod, Josh Pollack, Jennifer Chung, and Lex Nemzer, without whose many day and night hours the Tuneprint system could never have been implemented. In addition, we wish to acknowledge Sage Hill Partners, who funded the development of Tuneprint.

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