• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.200-206
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• 2024

Computing music similarity is an indispensable component in developing music search service. This paper proposes a relevance weight of each chromagram vector for cover song identification in computing a music similarity function in order to boost identification accuracy. We derive a music similarity function using the relevance weight based on the probabilistic relevance model, where higher relevance weights are assigned to less frequently-occurring discriminant chromagram vectors while lower weights to more frequently-occurring ones. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.518-524
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• 2018

The cover song refers to live recordings or reproduced albums. This paper studies two-dimensional Fourier transform as a feature-dimension reduction method to search cover song fast. The two-dimensional Fourier transform is conducive in feature-dimension reduction for cover song search due to musical-key invariance. This paper extends the previous work, which only utilize the magnitude of the Fourier transform, by introducing an invariant from phase based on the assumption that adjacent frames have the same musical-key change. We compare the cover song retrieval accuracy of the Fourier-transform based methods over two datasets. The experimental results show that the addition of the invariant from phase improves the cover song retrieval accuracy over the previous magnitude-only method.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.436-441
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• 2017

Computing music similarity is indispensable in constructing music retrieval system. This paper focuses on the cover song search among various music-retrieval tasks. We investigate the cover song search method based on the chroma n-gram to reduce storage for feature DB and enhance search accuracy. Specifically we propose t-tab n-gram, n-gram selection method, and n-gram set comparison method. Experiments on the widely used music dataset confirmed that the proposed method improves cover song search accuracy as well as reduces feature storage.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.662-667
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• 2022

Computing music similarity is an indispensable component in developing music search service. This paper focuses on learning a music similarity function in order to boost cover song identification performance. By using the probabilistic linear discriminant analysis, we construct a latent music space where the distances between cover song pairs reduces while the distances between the non-cover song pairs increases. We derive a music similarity function by testing hypothesis, whether two songs share the same latent variable or not, using the probabilistic models with the assumption that observed music features are generated from the learned latent music space. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.314-319
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• 2019

Computing chromagram similarity is indispensable in constructing cover song identification system. This paper proposes a code-based chromagram similarity to reduce the computational and the storage costs for cover song identification. By learning a song-specific codebook, a chromagram sequence is converted into a code sequence, which results in the reduction of the feature storage cost. We build a lookup table over the learned codebooks to compute chromagram similarity efficiently. Experiments on two music datasets were performed to compare the proposed code-based similarity with the conventional one in terms of cover song search accuracy, feature storage, and computational cost.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.195-200
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• 2021

Due to the development of video sharing platforms, the amount of video uploads is exploding. Such videos often include various types of music, among which cover songs are included. In order to protect the copyright of music, an algorithm to find the original song of the cover song is essential. However, it is not easy to find the original song because the cover song is a modification of the composition, speed and overall structure of the original song. So far, there is no known effective algorithm for searching the original song of the cover song. In this paper, we propose an algorithm for searching the original song of the cover song using the inflection points of the melody line. Inflection points represent the characteristic points of change in the melody sequence. The proposed algorithm compares the original song and the cover song using the sequence of inflection points for the representative phrase of the original song. Since the characteristics of the representative phrase are used, even if the cover song is a song made by modifying the overall composition of the song, the algorithm's search performance is excellent. Also, since the proposed algorithm uses only the features of the inflection point sequence, the memory usage is very low. The efficiency of the algorithm was verified through performance evaluation.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1084-1086
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• 2020

동영상 공유 플랫폼의 발전으로 다양한 영상들이 업로드 되고 있는데, 영상에 사용된 음악의 저작권 행사를 위해서는 음악 검색 알고리즘의 역할이 매우 중요하다. 그러나, 커버곡과 같이 원곡을 변형하여 사용한 경우에는 단순한 음악 검색 알고리즘으로는 이러한 상황을 탐지하기가 쉽지 않다. 음악의 높낮이나 빠르기 등이 바뀌어도 검색 성능이 유지되어야 하지만, 현재까지 연구된 알고리즘들은 이러한 변화에 취약하기 때문이다. 이에 본 연구에서는 멜로디 라인의 변곡점을 활용한 커버곡 검색 알고리즘을 제안한다. 본 알고리즘은 멜로디 라인을 중심으로 하여 높낮이 변화에도 높은 성능을 유지하고, 변곡점을 이용하기 때문에 빠르기 변화에 강하고 사용되는 메모리 양은 적다는 장점이 있다.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.639-645
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• 2019

This paper proposes a salient chromagram components extraction method based on the temporal discrete cosine transform of a chromagram block to improve cover song retrieval accuracy. The proposed salient chromagram emphasizes tonal contents of music, which are well-preserved between an original song and its cover version, while reducing the effects of timbre difference. We apply the proposed salient chromagram extraction method as a preprocessing step for the Fourier-transform based cover song matching. Experiments on two cover song datasets confirm that the proposed salient chromagram improves the cover song search accuracy.