The Music Information Retrieval Evaluation eXchange: Some Observations and Insights

Downie, J. Stephen; Ehmann, Andreas F.; Bay, Mert; Jones, Matt

doi:10.1007/978-3-642-11674-2_5

Cited by 62 publications

(47 citation statements)

References 7 publications

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“…MIREX is an international communitybased framework for the formal evaluation of MIR systems and algorithms [74], [75]. Among other tasks, MIREX allows for the comparison of different algorithms for artist identification, genre classification, or music transcription.…”

Section: Mirex 2009 Evaluation a Methodologymentioning

confidence: 99%

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Unifying Low-Level and High-Level Music Similarity Measures

Bogdanov

Serrà

Wack

et al. 2011

IEEE Trans. Multimedia

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Abstract-Measuring music similarity is essential for multimedia retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content. First, a low-level measure based on tempo-related description. Second, a high-level semantic measure based on the inference of different musical dimensions by support vector machines. These dimensions include genre, culture, moods, instruments, rhythm, and tempo annotations. Third, a hybrid measure which combines the above-mentioned distance measures with two existing lowlevel measures: a Euclidean distance based on principal component analysis of timbral, temporal, and tonal descriptors, and a timbral distance based on single Gaussian MFCC modeling. We evaluate our proposed measures against a number of baseline measures. We do this objectively based on a comprehensive set of music collections, and subjectively based on listeners' ratings. Results show that the proposed methods achieve accuracies comparable to the baseline approaches in the case of the tempo and classifier-based measures. The highest accuracies are obtained by the hybrid distance. Furthermore, the proposed classifier-based approach opens up the possibility to explore distance measures that are based on semantic notions.

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Section: Mirex 2009 Evaluation a Methodologymentioning

confidence: 99%

“…excerpts), which were chosen from IMIRSEL's 10 collections [75] and pertained to 10 different genres. For each participant's approach, a 7000×7000 distance matrix was calculated.…”

Section: Mirex 2009 Evaluation a Methodologymentioning

confidence: 99%

Unifying Low-Level and High-Level Music Similarity Measures

Bogdanov

Serrà

Wack

et al. 2011

IEEE Trans. Multimedia

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“…The Music Information Retrieval Evaluation eXchange (MIREX Downie, 2008;Downie, Ehmann, Bay, & Jones, 2010) is a framework for the formal evaluation of MIR systems, consisting of a number of tasks defined each year by the MIR community. Tasks include artist identification, mood/genre classification, beat tracking and melody extraction.…”

Section: Validation With the Mirex 2005 Datasetmentioning

confidence: 99%

Compression-based Modelling of Musical Similarity Perception

Pearce

Müllensiefen

2017

Journal of New Music Research

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Similarity is an important concept in music cognition research since the similarity between (parts of) musical pieces determines perception of stylistic categories and structural relationships between parts of musical works. The purpose of the present research is to develop and test models of musical similarity perception inspired by a transformational approach which conceives of similarity between two perceptual objects in terms of the complexity of the cognitive operations required to transform the representation of the first object into that of the second, a process which has been formulated in informationtheoretic terms. Specifically, computational simulations are developed based on compression distance in which a probabilistic model is trained on one piece of music and then used to predict, or compress, the notes in a second piece. The more predictable the second piece according to the model, the more efficiently it can be encoded and the greater the similarity between the two pieces. The present research extends an existing information-theoretic model of auditory expectation (IDyOM) to compute compression distances varying in symmetry and normalisation using high-level symbolic features representing aspects of pitch and rhythmic structure. Comparing these compression distances with listeners' similarity ratings between pairs of melodies collected in three experiments demonstrates that the compression-based model provides a good fit to the data and allows the identification of representations, model parameters and compression-based metrics that best account for musical similarity perception. The compression-based model also shows comparable performance to the best-performing algorithms on the MIREX 2005 melodic similarity task.

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“…From the year after, a very important evaluation campaign for this research was started by the University of Illinois: the Music Information Retrieval Evaluation eXchange (MIREX) [4]. Due to copyright restrictions, the organizers of the MIREX can only distribute publicly available test collections.…”

Section: Related Initiativesmentioning

confidence: 99%

MusiClef: Multimodal Music Tagging Task

Orio

Liem

Peeters

et al. 2012

Lecture Notes in Computer Science

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Abstract. MusiClef is a multimodal music benchmarking initiative that will be running a MediaEval 2012 Brave New Task on Multimodal Music Tagging. This paper describes the setup of this task, showing how it complements existing benchmarking initiatives and fosters less explored methodological directions in Music Information Retrieval. MusiClef deals with a concrete use case, encourages multimodal approaches based on these, and strives for transparency of results as much as possible. Transparency is encouraged at several levels and stages, from the feature extraction procedure up to the evaluation phase, in which a dedicated categorization of ground truth tags will be used to deepen the understanding of the relation between the proposed approaches and experimental results.

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The Music Information Retrieval Evaluation eXchange: Some Observations and Insights

Cited by 62 publications

References 7 publications

Unifying Low-Level and High-Level Music Similarity Measures

Unifying Low-Level and High-Level Music Similarity Measures

Compression-based Modelling of Musical Similarity Perception

MusiClef: Multimodal Music Tagging Task

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