Investigating Music Collections at Different Scales with AudioDB

Rhodes, Christophe; Crawford, Tim; Casey, Michael A.; d’Inverno, Mark

doi:10.1080/09298215.2010.516832

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…Chroma-time representations, or chromagrams, are popular for music information This research was funded by EPSRC Program Grant EP/L019981/1 and AHRC Grant AH/L006820/1. retrieval tasks, such as chord recognition [11], key estimation [12], and audio similarity search [13]. Typically the salience of each chroma is defined by summation of windowed elements of a spectrum that are assigned to a given pitch class.…”

Section: Introductionmentioning

confidence: 99%

Compositional chroma estimation using powered Euclidean distance

O'Hanlon

Sandler

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Chroma features are a popular tool in musical signal processing and information retrieval tasks, providing a compact representation of the tonal content of a piece of music. A variety of approaches to chroma estimation have been proposed, most of which rely on the summation of related frequency partials. However, frequency partials may be incorrectly assigned due to the log/linear relationship of frequency and pitch. Variations of chroma employing overtone suppression strategies are found in the literature. We propose a compositional model of chroma, which considers a coarse modelling of the effects of overtones in the expected chroma vectors of single notes. Synthetic chord recognition experiments indicate the usefulness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Compositional chroma estimation using powered Euclidean distance

O'Hanlon

Sandler

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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“…While the Segment Ontology (modeled in RDF-OWL 3 ) that follows is the backbone of our approach, it is only a mechanism to facilitate our overall method: recognizing that there can, and should, be many models of domain specific knowledge, and that music-generic and high-level relationships be used to move across these boundaries and make links between the knowledge within. As such, we use Segments as a music-generic dimension between explicitly temporal and implicitly (or indirectly) temporal concepts (and ontologies).…”

Section: Ontology and Approachmentioning

confidence: 99%

“…2. Domain-specific MIR tasks: parts of the model that relate to an MIR task, such as the elements extracted by a feature extractor, common labels from a classifier, distance metrics from a system such as [3]. 3.…”

Section: Foundational Conceptsmentioning

confidence: 99%

The segment ontology: Bridging music-generic and domain-specific

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Roure

et al. 2011

2011 IEEE International Conference on Multimedia and Expo

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Existing semantic representations of music analysis encapsulate narrow sub-domain concepts and are frequently scoped by the context of a particular MIR task. Segmentation is a crucial abstraction in the investigation of phenomena which unfold over time; we present a Segment Ontology as the backbone of an approach that models properties from the musicological domain independently from MIR implementations and their signal processing foundations, whilst maintaining an accurate and complete description of the relationships linking them. This framework provides two principal advantages which we explore through several examples: a layered separation of concerns that aligns the model with the needs of the users and systems that consume and produce the data; and the ability to link multiple analyses of differing types through transforms to and from the Segment axis.

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“…The use of MIR at different scales for musicology has been addressed by [7] in the context of an experimental system which is no longer available. More recently, several web-based systems have been developed for presenting collections of ethnomusicological recordings.…”

Section: Introductionmentioning

confidence: 99%

Digital music lab: A framework for analysing big music data

Abdallah

Benetos

Gold

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Abstract-In the transition from traditional to digital musicology, large scale music data are increasingly becoming available which require research methods that work on the collection level and at scale. In the Digital Music Lab (DML) project, a software system has been developed that provides large-scale analysis of music audio with an interactive interface. The DML system includes distributed processing of audio and other music data, remote analysis of copyright-restricted data, logical inference on the extracted information and metadata, and visual webbased interfaces for exploring and querying music collections. A system prototype has been set up in collaboration with the British Library and I Like Music Ltd, which has been used to analyse a diverse corpus of over 250,000 music recordings. In this paper we describe the system requirements, architecture, components, and data sources, explaining their interaction. Use cases and applications with initial evaluations of the proposed system are also reported.

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Investigating Music Collections at Different Scales with AudioDB

Cited by 11 publications

References 21 publications

Compositional chroma estimation using powered Euclidean distance

Compositional chroma estimation using powered Euclidean distance

The segment ontology: Bridging music-generic and domain-specific

Digital music lab: A framework for analysing big music data

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