Known-Artist Live Song Identification Using Audio Hashprints

Abstract

The goal of live song identification is to allow concertgoers to identify a live performance by recording a few seconds of the performance on their cell phone. This paper proposes a multistep approach to address this problem for popular bands. In the first step, GPS data are used to associate the audio query with a concert in order to infer who the musical artist is. This reduces the search space to a dataset containing the artist's studio recordings. In the next step, the known-artist search is solved by representing the audio as a sequence of binary codes called hashprints, which can be efficiently matched against the database using a two-stage cross-correlation approach. The hashprint representation is derived from a set of spectrotemporal filters that are learned in an unsupervised artist-specific manner. On the Gracenote live song identification benchmark, the proposed system outperforms five other baseline systems and improves the mean reciprocal rank of the previous state of the art from 0.68 to 0.79, while simultaneously reducing the average runtime per query from 10 to 0.9 s. We conduct extensive analyses of major factors affecting system performance.