Local summarization and multi-level LSH for retrieving multi-variant audio tracks

Yu, Yi; Crucianu, Michel; Oria, Vincent; Chen, Lei

doi:10.1145/1631272.1631320

Cited by 12 publications

(16 citation statements)

References 12 publications

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“…This method makes a tradeoff between accuracy and conciseness. The music signal is divided into multiple segments so that features within each segment are highly correlated and the statistics remain almost unchanged [16]. Then, a summary is computed for each segment.…”

Section: Related Workmentioning

confidence: 99%

Recognition and Summarization of Chord Progressions and Their Application to Music Information Retrieval

Zimmermann

Wang

et al. 2012

2012 IEEE International Symposium on Multimedia

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Accurate and compact representation of music signals is a key component of large-scale content-based music applications such as music content management and near duplicate audio detection. This problem is not well solved yet despite many research efforts in this field. In this paper, we suggest mid-level summarization of music signals based on chord progressions. More specially, in our proposed algorithm, chord progressions are recognized from music signals based on a supervised learning model, and recognition accuracy is improved by locally probing n-best candidates. By investigating the properties of chord progressions, we further calculate a histogram from the probed chord progressions as a summary of the music signal. We show that the chord progression-based summarization is a powerful feature descriptor for representing harmonic progressions and tonal structures of music signals. The proposed algorithm is evaluated with content-based music retrieval as a typical application. The experimental results on a dataset with more than 70,000 songs confirm that our algorithm can effectively improve summarization accuracy of musical audio contents and retrieval performance, and enhance music retrieval applications on large-scale audio databases.

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Section: Related Workmentioning

confidence: 99%

Recognition and Summarization of Chord Progressions and Their Application to Music Information Retrieval

Zimmermann

Wang

et al. 2012

2012 IEEE International Symposium on Multimedia

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“…As more nodes are added to a cluster, the probability of them all generating the same signature, unless they are a perfect clique, will decrease. This difficulty has led to different variants of LSH, such as multi-level LSH [31]. In this paper, we introduce a different solution, modification of the hashwords, which fits well with the graph clustering problem we are trying to solve.…”

Section: Lsh Applied To Graphsmentioning

confidence: 99%

Scalable discovery of best clusters on large graphs

Macropol

Singh

2010

Proc. VLDB Endow.

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The identification of clusters, well-connected components in a graph, is useful in many applications from biological function prediction to social community detection. However, finding these clusters can be difficult as graph sizes increase. Most current graph clustering algorithms scale poorly in terms of time or memory. An important insight is that many clustering applications need only the subset of best clusters, and not all clusters in the entire graph. In this paper we propose a new technique, Top Graph Clusters (TopGC), which probabilistically searches large, edge weighted, directed graphs for their best clusters in linear time. The algorithm is inherently parallelizable, and is able to find variable size, overlapping clusters. To increase scalability, a parameter is introduced that controls memory use. When compared with three other state-of-the art clustering techniques, TopGC achieves running time speedups of up to 70% on large scale real world datasets. In addition, the clusters returned by TopGC are consistently found to be better both in calculated score and when compared on real world benchmarks.

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“…MIR has gradually developed into an interdisciplinary research field that is related to audio signal processing [8,9], audio content indexing [1,2,3,4,7,10], sequence matching [8], pattern recognition [11], music retrieval evaluation [12], etc. Applications of queryby-audio MIR include near duplicate audio detection [13], audio-based music plagiarism analysis [14] and query-byexample/humming/singing [1,10].…”

Section: Background and Related Workmentioning

confidence: 99%

“…An actual LSH design for musical content depends on the representations of musical audio features. A brief survey of scalable music content retrieval can be found in [7].…”

Section: Introductionmentioning

confidence: 99%

Combining multi-probe histogram and order-statistics based LSH for scalable audio content retrieval

Yu¹,

Crucianu²,

Oria³

et al. 2010

Proceedings of the 18th ACM International Conference on Multimedia

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In order to improve the reliability and the scalability of content-based retrieval of variant audio tracks from large music databases, we suggest a new multi-stage LSH scheme that consists in (i) extracting compact but accurate representations from audio tracks by exploiting the LSH idea to summarize audio tracks, and (ii) adequately organizing the resulting representations in LSH tables, retaining almost the same accuracy as an exact kNN retrieval. In the first stage, we use major bins of successive chroma features to calculate a multi-probe histogram (MPH) that is concise but retains the information about local temporal correlations. In the second stage, based on the order statistics (OS) of the MPH, we propose a new LSH scheme, OS-LSH, to organize and probe the histograms. The representation and organization of the audio tracks are storage efficient and support robust and scalable retrieval. Extensive experiments over a large dataset with 30,000 real audio tracks confirm the effectiveness and efficiency of the proposed scheme.

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Local summarization and multi-level LSH for retrieving multi-variant audio tracks

Cited by 12 publications

References 12 publications

Recognition and Summarization of Chord Progressions and Their Application to Music Information Retrieval

Recognition and Summarization of Chord Progressions and Their Application to Music Information Retrieval

Scalable discovery of best clusters on large graphs

Combining multi-probe histogram and order-statistics based LSH for scalable audio content retrieval

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