Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification

• Published in: IEEE transactions on audio, speech, and language processing speech, and language processing, 2008-08, Vol.16 (6), p.1138-1151
• Main Authors: Serra, J., Gomez, E., Herrera, P., Serra, X.
• Format: Article
• Language: English
• Subjects: Acoustic signal analysis; Alignment; Applied sciences; Audio signals; Digital audio players; Dynamic programming; Dynamic tests; Enregistrament i reproducció; Exact sciences and technology; Hard disks; Information retrieval; Information theory; Information, signal and communications theory; Miscellaneous; Multidimensional sequences; Multidimensional systems; Multiple signal classification; Music; Music information retrieval; Música; Performance analysis; Signal analysis; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Similarity; State of the art; Studies; Tasks; Telecommunications and information theory; Time factors; Tonalitat (Música); Tracking; Tècniques digitals
• ISSN: 1558-7916; 2329-9290; 1558-7924; 2329-9304
• DOI: 10.1109/TASL.2008.924595

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms. This paper first presents a series of experiments carried out with two state-of-the-art methods for cover song identification. We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or dynamic time warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best performing ones are finally applied to the newly proposed method. Multiple evaluations of this one confirm a large increase in identification accuracy when comparing it with alternative state-of-the-art approaches.