Harvesting and Structuring Social Data in Music Information Retrieval

Oramas, Sergio

doi:10.1007/978-3-319-07443-6_55

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Although some existing repositories with music information, such as Wikipedia 2 , Oxford Music Online 3 , or MusicBrainz 4 are quite complete and accurate, there is still a vast amount of music information out there that is generally scattered across different sources on the Web. Selecting the sources and harvesting and combining data is a crucial step towards the creation of practical and meaningful music research corpora (Oramas, 2014) In this work, three different datasets are built as testbeds of our knowledge extraction methodologies. First, we illustrate in detail a methodology for selecting and mixing data coming from different sources in the creation of a flamenco music knowledge base.…”

Section: Collecting Text Corporamentioning

confidence: 99%

Natural language processing for music knowledge discovery

Oramas

Espinosa-Anke

Gómez

et al. 2018

Journal of New Music Research

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Today, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago. In this work, we present different Natural Language Processing (NLP) approaches to harness the potential of these text collections for automatic music knowledge discovery, covering different phases in a prototypical NLP pipeline, namely corpus compilation, text-mining, information extraction, knowledge graph generation and sentiment analysis. Each of these approaches is presented alongside different use cases (i.e., flamenco, Renaissance and popular music) where large collections of documents are processed, and conclusions stemming from data-driven analyses are presented and discussed.

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Section: Collecting Text Corporamentioning

confidence: 99%

Natural language processing for music knowledge discovery

Oramas

Espinosa-Anke

Gómez

et al. 2018

Journal of New Music Research

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“…It allows describing music creation workflows, temporal events and editorial metadata. Recently, Oramas proposed a methodology to structure the social media content using the communitygenerated data such as tags, with formal knowledge representations [8].…”

Section: State Of the Artmentioning

confidence: 99%

Culture-Aware Approaches to Modeling and Description of Intonation Using Multimodal Data

Koduri

2015

Lecture Notes in Computer Science

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Abstract. Computational approaches that conform to the cultural context are of paramount importance in music information research. The current state-of-theart has a limited view of such context, which manifests in our ontologies, data-, cognition-and interaction-models that are biased to the market-driven popular music. In a step towards addressing this, the thesis draws upon multimodal data sources concerning art music traditions, extracting culturally relevant and musically meaningful information about melodic intervals from each of them and structuring it with formal knowledge representations. As part of this, we propose novel approaches to describe intonation in audio music recordings and to use and adapt the semantic web infrastructure to complement this with the knowledge extracted from text data. Due to the complementary nature of the data sources, structuring and linking the extracted information results in a symbiosis mutually enriching their information. Over this multimodal knowledge base, we propose similarity measures for the discovery of musical entities, yielding a culturallysound navigation space.

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“…Musical Information Retrieval (MIR) will work on unstructured and structured data. Structured and unstructured data will get from web and micro-blogging sites respectively [24].…”

Section: Related Workmentioning

confidence: 99%