Scalable, Content-Based Audio Identification by Multiple Independent Psychoacoustic Matching
A software system for content-based identification of audio recordings is presented. The system 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 of both the instantaneous pattern of a recording's perceptual features and the information contained in the evolution of these features over time, the system 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 the system has matched all queries successfully against a database of 100 000 commercially released recordings.
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