Identification of cover songs using information theoretic measures of similarity

Foster, Peter; Dixon, Simon; Klapuri, Anssi

doi:10.1109/icassp.2013.6637746

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…We provide an evaluation of competing information-theoretic approaches and identify issues concerning their implementation. This paper extends our previous work [13] as follows: Firstly, we examine a larger set of distance measures and estimate distance measures by predicting discrete-valued sequences. Further, we incorporate the Million Song dataset (MSD) [25] into our evaluations.…”

Section: Introductionmentioning

confidence: 78%

“…Chroma features are averaged over beat intervals, before applying square-root compression and normalising chroma features with respect to the Euclidean norm. Based on our previous work [13], we evaluate using a PBR of 240 beats per minute (bpm).…”

Section: A Feature Extractionmentioning

confidence: 99%

“…We then select the clustering which minimises the mean squared error between data points and assigned clusters. The described quantisation method performs similarly to an alternative based on pairwise sequence quantisation; for a detailed discussion we refer to our previous work [13].…”

Section: Quantisationmentioning

confidence: 99%

“…Based on our previous work [13], we consider an information-theoretic approach to quantifying similarity between feature vector sequences. One possible approach based on the non-Shannon information measure of Kolmogorov complexity [14], the normalised compression distance (NCD) [15], quantifies similarity between two strings in terms of joint compressibility.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Cover Songs Using Information-Theoretic Measures of Similarity

Foster

Dixon

Klapuri

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper investigates methods for quantifying similarity between audio signals, specifically for the task of of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantised audio features, to continuous-valued approaches. In the discrete case, we propose a method for computing the normalised compression distance, where we account for correlation between time series. In the continuous case, we propose to compute information-based measures of similarity as statistics of the prediction error between time series. We evaluate our methods on two cover song identification tasks using a data set comprised of 300 Jazz standards and using the Million Song Dataset. For both datasets, we observe that continuous-valued approaches outperform discrete-valued approaches. We consider approaches to estimating the normalised compression distance (NCD) based on string compression and prediction, where we observe that our proposed normalised compression distance with alignment (NCDA) improves average performance over NCD, for sequential compression algorithms. Finally, we demonstrate that continuousvalued distances may be combined to improve performance with respect to baseline approaches. Using a large-scale filter-andrefine approach, we demonstrate state-of-the-art performance for cover song identification using the Million Song Dataset.

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Section: Introductionmentioning

confidence: 78%

Section: A Feature Extractionmentioning

confidence: 99%

Section: Quantisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Cover Songs Using Information-Theoretic Measures of Similarity

Foster

Dixon

Klapuri

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…. v t−1 at each time step t. We perform crosspredictions by determining sequential successors of nearest neighbours in the embedded space, using the approach given in [67]. As a distance measure between predicted and observed feature sequences, we compute the normalised mean square error [65].…”

Section: A Similarity Rating Predictionmentioning

confidence: 99%

Sequential Complexity as a Descriptor for Musical Similarity

Foster

Mauch

Dixon

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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We propose string compressibility as a descriptor of temporal structure in audio, for the purpose of determining musical similarity. Our descriptors are based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks. We base our evaluation on a dataset of 15500 track excerpts of Western popular music, for which we obtain 7800 web-sourced pairwise similarity ratings. To assess the agreement among similarity ratings, we perform an evaluation under controlled conditions, obtaining a rank correlation of 0.33 between intersected sets of ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio

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Triplet Convolutional Network for Music Version Identification

Yang

Chen

2018

MultiMedia Modeling

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Identification of cover songs using information theoretic measures of similarity

Cited by 3 publications

References 16 publications

Identifying Cover Songs Using Information-Theoretic Measures of Similarity

Identifying Cover Songs Using Information-Theoretic Measures of Similarity

Sequential Complexity as a Descriptor for Musical Similarity

Triplet Convolutional Network for Music Version Identification

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