2024-03-28T12:38:31Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/4024392024-03-15T10:58:04Zcom_10803_236col_10803_690279
nam a 5i 4500
Descripción de entonación
Entonación melódica
Música carnática
Datos multimodales
Base de conocimiento
Ontologías
Intonation description
Melodic intonation
Carnatic music
Multimodal data
Knowledge-base
Ontologies
Towards a multimodal knowledge base for Indian art music: a case study with melodic intonation
[Barcelona] :
Universitat Pompeu Fabra,
2017
Accés lliure
http://hdl.handle.net/10803/402439
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Koduri, Gopala Krishna,
autor
Programa de doctorat en Tecnologies de la Informació i les Comunicacions,
degree
1 recurs en línia (215 pàgines)
Tesi
Doctorat
Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions
2017
Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions
Tesis i dissertacions electròniques
Serra, Xavier,
supervisor acadèmic
TDX
This thesis is a result of our research efforts in building a multi-modal knowledgebase
for the specific case of Carnatic music. Besides making use of metadata
and symbolic notations, we process natural language text and audio data to extract
culturally relevant and musically meaningful information and structuring
it with formal knowledge representations. This process broadly consists of two
parts. In the first part, we analyze the audio recordings for intonation description
of pitches used in the performances. We conduct a thorough survey and
evaluation of the previously proposed pitch distribution based approaches on a
common dataset, outlining their merits and limitations. We propose a new data
model to describe pitches to overcome the shortcomings identified. This expands
the perspective of the note model in-vogue to cater to the conceptualization of
melodic space in Carnatic music. We put forward three different approaches to
retrieve compact description of pitches used in a given recording employing our
data model. We qualitatively evaluate our approaches comparing the representations
of pitched obtained from our approach with those from a manually labeled
dataset, showing that our data model and approaches have resulted in representations
that are very similar to the latter. Further, in a raaga classification task
on the largest Carnatic music dataset so far, two of our approaches are shown to
outperform the state-of-the-art by a statistically significant margin.
In the second part, we develop knowledge representations for various concepts
in Carnatic music, with a particular emphasis on the melodic framework. We
discuss the limitations of the current semantic web technologies in expressing
the order in sequential data that curtails the application of logical inference. We
present our use of rule languages to overcome this limitation to a certain extent.
We then use open information extraction systems to retrieve concepts, entities
and their relationships from natural language text concerning Carnatic music.
We evaluate these systems using the concepts and relations from knowledge representations we have developed, and groundtruth curated using Wikipedia data.
Thematic domains like Carnatic music have limited volume of data available online.
Considering that these systems are built forweb-scale data where repetitions
are taken advantage of, we compare their performances qualitatively and quantitatively,
emphasizing characteristics desired for cases such as this. The retrieved
concepts and entities are mapped to those in the metadata. In the final step, using
the knowledge representations developed, we publish and integrate the information
obtained from different modalities to a knowledge-base. On this resource,
we demonstrate how linking information from different modalities allows us to
deduce conclusions which otherwise would not have been possible.
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