An Evaluation of Feature Extraction for Query-by-Content Audio Information Retrieval

Yu, Yi; Downie, J. Stephen; Joe, Kazuki

doi:10.1109/ism.workshops.2007.57

Cited by 2 publications

(3 citation statements)

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“…To guarantee the detection accuracy, the popular detection methods use the DP [1][2] [3] or its variants [5] to perform audio sequence comparison.However, it is very time consuming since the feature (Pitch [2][3], Chroma [1], MFCC [3]) sequence of musical audio is very high dimensional. To speed up the audio feature sequence comparison, SFS is extracted from the audio sequences as a concise representation similar to [6].…”

Section: Related Techniquesmentioning

confidence: 99%

“…From the amplitude spectrum Pitch, MFCC and Mel-magnitude are also computed. The details about the feature extraction methods can be found in [1][2] [3]. Based on feature sets of Chroma, Pitch, MFCC and Mel-magnitude, a combined SFS feature can be extracted.…”

Section: Cosin Systemmentioning

confidence: 99%

“…To facilitate the comparison among the detection methods, three independent matching/searching schemes are included in this part. DP is a typical method to calculate the pair-wise sequence distance [2] [3], which can offer a high accuracy while taking long time for matching. KNN is a non-sequence-comparison, exhaustive searching method.…”

Section: Cosin Systemmentioning

confidence: 99%

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Cosin

Downie

Moerchen

et al. 2008

Proceedings of the 16th ACM International Conference on Multimedia

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We develop a content-based audio COver Song IdeNtification (COSIN) system to detect/group cover songs.The COSIN takes music audio content as input and performs similarity searching to locate variants of the input (i.e., cover versions). Identified cover songs are returned in the rank order according to their similarity to the input.The COSIN also incorporates a set of tools to evaluate retrieval performance so researchers can explore different retrieval schemes and parameters (e.g. recall, precision).The COSIN utilizes a suite of techniques to detect cover songs including: Pitch + Dynamic Programming (DP), Chroma + DP, and Semantic Feature Summarization (SFS) + Hash-Based Approximate Matching (HBAM). Demonstration system shows that COSIN is a very potential music content retrieval tool. Running some music retrieval schemes on COSIN platform, recent experiments with SFS + LSH Variants demonstrate a nicely balanced efficiency (search speed) v. performance (search accuracy) tradeoff.Content-based audio retrieval, cover songs, musical audio sequences summarization, hash-based indexing * This is a postgraduate project in NWU,

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Section: Related Techniquesmentioning

confidence: 99%

Section: Cosin Systemmentioning

confidence: 99%

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Cosin

Downie

Moerchen

et al. 2008

Proceedings of the 16th ACM International Conference on Multimedia

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A Music Retrieval Method Based on Distribution of Feature Segments

Ono

Suzuki

Kawagoe

2008

2008 Tenth IEEE International Symposium on Multimedia

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In this paper, we propose a music retrieval method based on the distributions of features in the music. In common music retrieval methods, if several features are similar between the query and the retrieval target, the retrieval systems return that the query is similar to the retrieval target. However, a problem is that several features in the music are ignored. If the other features in the query and the retrieval target are quite different, the query and the retrieval target should be treated as different types of music. Therefore, we calculate the importance of each feature in the music. Then, we compare the importance of features between the query and the retrieval target, and we can retrieve the music without ignoring the importance of several features. In our experimental evaluation, we can confirm that our proposed system has better accuracy than the baseline method.

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An Evaluation of Feature Extraction for Query-by-Content Audio Information Retrieval

Cited by 2 publications

References 1 publication

Cosin

Cosin

A Music Retrieval Method Based on Distribution of Feature Segments

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