Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach

Pickens, Jeremy; Bello, Juan Pablo; Monti, Giuliano; Sandler, Mark; Crawford, Tim; Dovey, Matthew; Byrd, Don

doi:10.1076/jnmr.32.2.223.16742

Cited by 41 publications

(23 citation statements)

References 12 publications

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“…The OMRAS2 project set out to follow in the footsteps of the trail-blazing OMRAS project (www.omras.org), which brought together partners from the USA and the UK and was funded by JISC (the Joint Infrastructure Sub-Committee of the Higher Education Funding Council of England-HEFCE) and the National Science Foundation (NSF). That project has been described in several papers, the most comprehensive of which is by Pickens et al (2003). Those of us involved in OMRAS and OMRAS2 have almost forgotten what the acronym stands for!…”

Section: Queen Marymentioning

confidence: 99%

Towards a Distributed Research Environment for Music Informatics and Computational Musicology

Dixon

Sandler

d’Inverno

et al. 2010

Journal of New Music Research

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Section: Queen Marymentioning

confidence: 99%

Towards a Distributed Research Environment for Music Informatics and Computational Musicology

Dixon

Sandler

d’Inverno

et al. 2010

Journal of New Music Research

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“…A successful approach at identifying harmonic similarities between audio and symbolic data was presented in [32]. It relied on automatic transcription, a process that is partially effective within a highly constrained subset of musical recordings (e.g.…”

Section: Harmonymentioning

confidence: 99%

SIMAC: semantic interaction with music audio contents

Herrera

Bello

Widmer

et al. 2005

2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005)

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The SIMAC project addresses the study and development of innovative components for a music information retrieval system. The key feature is the usage and exploitation of semantic descriptors of musical content that are automatically extracted from music audio files. These descriptors are generated in two ways: as derivations and combinations of lower-level descriptors and as generalizations induced from manually annotated databases by the intensive application of machine learning. The project aims also towards the empowering (i.e. adding value, improving effectiveness) of music consumption behaviours, especially of those that are guided by the concept of similarity.

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“…Examples and applications include automated score following [7] and melody-based information retrieval [1]. Notable exceptions to the monophonic domain are Raphael [17] and Pickens et al [16]. The former work uses carefully constructed hidden Markov models to automatically transcribe polyphonic audio piano music, treating the actual notes (pitch values) as the hidden states and using Viterbi to discover these values.…”

Section: Music Modelsmentioning

confidence: 99%

Polyphonic music modeling with random fields

Lavrenko

Pickens

2003

Proceedings of the Eleventh ACM International Conference on Multimedia

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Recent interest in the area of music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of recent developments in statistical modeling. In this paper we discuss an application of Random Fields to the problem of creating accurate yet flexible statistical models of polyphonic music. With such models in hand, the challenges of developing effective searching, browsing and organization techniques for the growing bodies of music collections may be successfully met. We offer an evaluation of these models in terms of perplexity and prediction accuracy, and show that random fields not only outperform Markov chains, but are much more robust in terms of overfitting. 1

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Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach

Cited by 41 publications

References 12 publications

Towards a Distributed Research Environment for Music Informatics and Computational Musicology

Towards a Distributed Research Environment for Music Informatics and Computational Musicology

SIMAC: semantic interaction with music audio contents

Polyphonic music modeling with random fields

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