Gopala K. Koduri and Sertan Şentürk defend their PhD thesis

22 Feb 2017

Wednesday, February 22nd 2017 at 11:00h in room 55.309 (Tanger Building, UPF Communication Campus)

Gopala K. Koduri: “Towards a multimodal knowledge base for Indian art music: A case study with melodic intonation”
Thesis director: Xavier Serra
Thesis Committee: Anja Volk (Utrecht University), Baris Bozkurt (Koç University) and George Fazekas (QMUL)
 
Abstract: This thesis is a result of our research efforts in building a multi-modal knowledge-base 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 for web-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.

Wednesday, February 22nd 2017 at 16:00h in room 55.309 (Tanger Building, UPF Communication Campus)

Sertan Şentürk: “Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music”
Thesis director: Xavier Serra
Thesis Committee: Gerhard Widmer (Johannes Kepler University), Baris Bozkurt (Koç University) and Tillman Weyde (City, University of London)
 
Abstract: This thesis addresses several shortcomings on the current state of the art methodologies in music information retrieval (MIR). In particular, it proposes several computational approaches to automatically analyze and describe music scores and audio recordings of Ottoman-Turkish makam music (OTMM). The main contributions of the thesis are the music corpus that has been created to carry out the research and the audio-score alignment methodology developed for the analysis of the corpus. In addition, several novel computational analysis methodologies are presented in the context of common MIR tasks of relevance for OTMM. Some example tasks are predominant melody extraction, tonic identification, tempo estimation, makam recognition, tuning analysis, structural analysis and melodic progression analysis. These methodologies become a part of a complete system called Dunya-makam for the exploration of large corpora of OTMM.
 
The thesis starts by presenting the created CompMusic Ottoman-Turkish makam music corpus. The corpus includes 2200 music scores, more than 6500 audio recordings, and accompanying metadata. The data has been collected, annotated and curated with the help of music experts. Using criteria such as completeness, coverage and quality, we validate the corpus and show its research potential. In fact, our corpus is the largest and most representative resource of OTMM that can be used for computational research. Several test datasets have also been created from the corpus to develop and evaluate the specific methodologies proposed for different computational tasks addressed in the thesis.
 
The part focusing on the analysis of music scores is centered on phrase and section level structural analysis. Phrase boundaries are automatically identified using an existing state-of-the-art segmentation methodology. Section boundaries are extracted using heuristics specific to the formatting of the music scores. Subsequently, a novel method based on graph analysis is used to establish similarities across these structural elements in terms of melody and lyrics, and to label the relations semiotically. 
 
The audio analysis section of the thesis reviews the state-of-the-art for analysing the melodic aspects of performances of OTMM. It proposes adaptations of existing predominant melody extraction methods tailored to OTMM. It also presents improvements over pitch-distribution-based tonic identification and makam recognition methodologies. 
 
The audio-score alignment methodology is the core of the thesis. It addresses the culture-specific challenges posed by the musical characteristics, music theory related representations and oral praxis of OTMM. Based on several techniques such as subsequence dynamic time warping, Hough transform and variable-length Markov models, the audio-score alignment methodology is designed to handle the structural differences between music scores and audio recordings. The method is robust to the presence of non-notated melodic expressions, tempo deviations within the music performances, and differences in tonic and tuning. The methodology utilizes the outputs of the score and audio analysis, and links the audio and the symbolic data. In addition, the alignment methodology is used to obtain score-informed description of audio recordings. The score-informed audio analysis not only simplifies the audio feature extraction steps that would require sophisticated audio processing approaches, but also substantially improves the performance compared with results obtained from the state-of-the-art methods solely relying on audio data.
 
The analysis methodologies presented in the thesis are applied to the CompMusic Ottoman-Turkish makam music corpus and integrated into a web application aimed at culture-aware music discovery. Some of the methodologies have already been applied to other music traditions such as Hindustani, Carnatic and Greek music. Following open research best practices, all the created data, software tools and analysis results are openly available. The methodologies, the tools and the corpus itself provide vast opportunities for future research in many fields such as music information retrieval, computational musicology and music education.
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