Visualizing music and audio using self-similarity

Foote, Jonathan

doi:10.1145/319463.319472

Cited by 222 publications

(163 citation statements)

References 3 publications

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“…Therefore, we realize two consecutive approaches to reveal the differences between the feature vectors. The first one is to normalize the values of similarity matrix σ between 0 and 1 to obtain a uniform distribution (5) and the second one is to strengthen the diagonal stripes by performing a diagonal summation process over the matrix (6).…”

Section: Post Processingmentioning

confidence: 99%

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Generating Expressive Summaries for Speech and Musical Audio using Self-Similarity Clues

Sert

Baykal

Yazıcı

2006

2006 IEEE International Conference on Multimedia and Expo

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We present a novel algorithm for structural analysis of audio to detect repetitive patterns that are suitable for content-based audio information retrieval systems, since repetitive patterns can provide valuable information about the content of audio, such as a chorus or a concept. The Audio Spectrum Flatness (ASF) feature of the MPEG-7 standard, although not having been considered as much as other feature types, has been utilized and evaluated as the underlying feature set. Expressive summaries are chosen as the longest patterns by the k-means clustering algorithm. Proposed approach is evaluated on a test bed consisting of popular song and speech clips based on the ASF feature. The well known Mel Frequency Cepstral Coefficients (MFCCs) are also considered in the experiments for the evaluation of features. Experiments show that, all the repetitive patterns and their locations are obtained with the accuracy of 93% and 78% for music and speech, respectively.

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Section: Post Processingmentioning

confidence: 99%

“…Cooper and Foote [5] presented analytic methods to find repetitive structure in musical audio. They proposed a representation, formally known as similarity matrix, to analyze the structure of audio [6,7]. However, none of the previous methods addressed the problem of detecting all the chorus sections in a song.…”

Section: Introductionmentioning

confidence: 99%

Generating Expressive Summaries for Speech and Musical Audio using Self-Similarity Clues

Sert

Baykal

Yazıcı

2006

2006 IEEE International Conference on Multimedia and Expo

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“…However, even so the process is not accurate. The focus of research in this field is the identification of new techniques for musical transcription from audio waves [27] and use of non-melodic features for matching, such as structural information [10,11].…”

Section: Other Mir Projectsmentioning

confidence: 99%

An architecture for effective music information retrieval

Uitdenbogerd¹,

Zobel²

2004

J. Am. Soc. Inf. Sci.

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We have explored methods for music information retrieval for polyphonic music stored in the MIDI format. These methods use a query, expressed as a series of notes that are intended to represent a melody or theme, to identify similar pieces. Our work has shown that a three-phase architecture is appropriate for this task, in which the first phase is melody extraction, the second is standardisation, and the third is query-to-melody matching. We have investigated and systematically compared algorithms for each of these phases. To ensure that our results are robust, we have applied methodologies that are derived from text information retrieval: we developed test collections and compared different ways of acquiring test queries and relevance judgements. In this paper we review this program of work, compare to other approaches to music information retrieval, and identify outstanding issues.

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“…Foote and Cooper [7] constructed a similarity matrix from midi and audio data, and Wakefield and Bartsch created a similar structure using spectral data. [2,3] This has inspired some of our work.…”

Section: Related Workmentioning

confidence: 99%

Discovering Musical Structure in Audio Recordings

Dannenberg

2002

Music and Artificial Intelligence

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Abstract. Music is often described in terms of the structure of repeated phrases. For example, many songs have the form AABA, where each letter represents an instance of a phrase. This research aims to construct descriptions or explanations of music in this form, using only audio recordings as input. A system of programs is described that transcribes the melody of a recording, identifies similar segments, clusters these segments to form patterns, and then constructs an explanation of the music in terms of these patterns. Additional work using spectral information rather than melodic transcription is also described. Examples of successful machine "listening" and music analysis are presented.

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Visualizing music and audio using self-similarity

Cited by 222 publications

References 3 publications

Generating Expressive Summaries for Speech and Musical Audio using Self-Similarity Clues

Generating Expressive Summaries for Speech and Musical Audio using Self-Similarity Clues

An architecture for effective music information retrieval

Discovering Musical Structure in Audio Recordings

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