Identifying 'Cover Songs' with Chroma Features and Dynamic Programming Beat Tracking

Large music collections, ranging from thousands to millions of tracks, are unsuited to manual searching, motivating the development of automatic search methods. When different musicians perform the same underlying song or piece, these are known as 'cover' versions. We describe a system that attempts to identify such a relationship between music audio recordings. To overcome variability in tempo, we use beat tracking to describe each piece with one feature vector per beat. To deal with variation in instrumentation, we use 12-dimensional 'chroma' feature vectors that collect spectral energy supporting each semitone of the octave. To compare two recordings, we simply cross-correlate the entire beat-by-chroma representation for two tracks and look for sharp peaks indicating good local alignment between the pieces. Evaluation on several databases indicate good performance, including best performance on an independent international evaluation, where the system achieved a mean reciprocal ranking of 0.49 for true cover versions among top-10 returns

IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY

13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio

Performance Following: Real-Time Prediction of Musical Sequences Without a Score

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AMS and genre classification using timbre, rhythm and tempo models

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Identifying "Cover Songs" with Beat-Synchronous Chroma Features

Describes the problem of cover songs, how to calculate chroma features and track beats with dynamic programming, and how to match beat-chroma matrices

Large-Scale Cover Song Recognition Using the 2D Fourier Transform Magnitude

Large-scale cover song recognition involves calculating item-to-item similarities that can accommodate differences in timing and tempo, rendering simple Euclidean measures unsuitable. Expensive solutions such as dynamic time warping do not scale to million of instances, making them inappropriate for commercial-scale applications. In this work, we transform a beat-synchronous chroma matrix with a 2D Fourier transform and show that the resulting representation has properties that fit the cover song recognition task. We can also apply PCA to efficiently scale comparisons. We report the best results to date on the largest available dataset of around 18,000 cover songs amid one million tracks, giving a mean average precision of 3.0%

Perceptually-Inspired Music Audio Analysis

Covers a few aspects of music audio processing, attempting to link them to what we know of human auditory scene analysis

Beat-Synchronous Chroma Representations for Music Analysis

Discusses work with cover songs by the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, with discussion of other applications of the beat-chroma representation