Two-layer large-scale cover song identification system based on music structure segmentation

Cai, Kang; Yang, Dingcheng; Chen, Xiaoou

doi:10.1109/mmsp.2016.7813372

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Finally, a third direction was to combine different methods in a two-step pruning approach: the first step aimed to discard tracks from the corpus that could be easily distinguished as non-versions using scalable systems [25] or "weak rejectors" [18], while the second step involved a more accurate method operating on the remaining candidates (see Section IV-D).…”

Section: -2015: Improving Accuracy and Scalabilitymentioning

confidence: 99%

“…Segmenting sections -An intuitive solution for dealing with structural changes is to first perform segmentation in order to identify sections and later estimate the segment-wise similarities between two tracks [9], [25]. Although this is an ideal solution for achieving structure invariance, segmentation algorithms are currently error-prone, and comparing wrongly segmented sections may drastically reduce the system performance.…”

Section: Structure Invariancementioning

confidence: 99%

“…Scalable VI systems -Lately, deep learning-based VI systems have made substantial contributions for bridging the accuracy-scalability gap, but before them, scalable VI systems were not sufficient for obtaining confident results. However, considering their far-from-random performances, such early systems became a natural choice to be used as the first step of pruning-based, multi-step systems [25]. The general tendency was to use systems that encode tracks into compact embedding vectors as the first step, mainly to take advantage of the fast lookup times.…”

Section: Pruningmentioning

confidence: 99%

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Audio-based Musical Version Identification: Elements and Challenges

Yesiler,

Doras,

Bittner

et al. 2021

Preprint

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Creating novel interpretations of existing musical compositions is and has always been an essential part of musical practice. Before the advent of recorded music, listening to a piece of music mostly meant listening to a version of it, in many cases, performed by musicians other than the original composer or performer. While this practice, along with musical notation, allows compositions to remain known for many decades or even centuries, it also provides room for artistic expression. Oftentimes, versions that are reproduced faithfully with respect to the original composition are seen as tributes to honor the composers; however, versions that are altered with the limitless creativity of humans often demonstrate how an existing idea can be transformed into something that goes beyond the original intention. Regardless of the ways in which musical versions are created, they are fundamental to the world's musical heritage.In this article, we consider a musical version to be "any rendition or performance of an existing musical composition." Another widely used term in the literature is "cover songs"; however, there are three main reasons we avoid using that term in this article. Firstly, the scope of the term is not clear as many authors limit its use to certain genres (e.g., pop and rock) and time periods (e.g., after the 1950s). Secondly, it has negative historical and economic connotations that date back to when the term emerged: record labels often asked White musicians to record (or cover) the songs of Black musicians as a marketing strategy to reach certain demographics. Lastly, the term does not represent the wide range of musical versions that are the subject of this article (see Fig. 1). Therefore, we employ the term "musical versions" throughout this article for being as inclusive as possible.This survey article focuses on the literature studying musical version identification and retrieval from a computational perspective. A key concept we refer to throughout this article is the concept of similarity, which we aim to quantify and model in a way that would reflect musical versions as semantically closer than non-versions. Following the literature, we consider that all versions of the same composition belong to the same group, or clique.

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Section: -2015: Improving Accuracy and Scalabilitymentioning

confidence: 99%

Section: Structure Invariancementioning

confidence: 99%

Section: Pruningmentioning

confidence: 99%

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Audio-based Musical Version Identification: Elements and Challenges

Yesiler,

Doras,

Bittner

et al. 2021

Preprint

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“…This chroma can be used to recognize a cover song with 62% accuracy [5]- [7]. Other experiments used pitch [8]- [10], Information-Theoretic Measures of Similarity [11], music structure segmentation [12] and 2D Fourier Transform [13] to recognize cover songs. In a previous experiment, fingerprinting has been used to identify the title of a song based on the raw signal of the music.…”

Section: Introductionmentioning

confidence: 99%

Music fingerprinting based on bhattacharya distance for song and cover song recognition

Sarno¹,

Wijaya²,

Mahardika³

2019

IJECE

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People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.

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Deep Learning for Cover Song Apperception

Derhami¹,

Bhajantri²

2020

Advances in Intelligent Systems and Computing

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Two-layer large-scale cover song identification system based on music structure segmentation

Cited by 5 publications

References 14 publications

Audio-based Musical Version Identification: Elements and Challenges

Audio-based Musical Version Identification: Elements and Challenges

Music fingerprinting based on bhattacharya distance for song and cover song recognition

Deep Learning for Cover Song Apperception

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