Date: December 13th, 2013
Venue: CS 25, Dept of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai (India)
Scientific Committee: Xavier Serra (UPF), Preeti Rao (IITB), Hema Murthy (IITM)
This workshop aims to give an overview of the research being done within the CompMusic project related to Hindustani and Carnatic music. The workshop is followed, on the next day, by a special seminar dedicated to the discussion of the tools that can be used for the exploration and appreciation of Carnatic music, and on December 15th there will be a Lecture Demonstration by Padma Vibhushan Sangita Kalanidhi Mrudangam Legend Dr. Umayalpuram K Sivaraman. Entrance to all the events is free but please confirm your attendance to Prof. Hema Murthy.
09:00h Xavier Serra: “CompMusic research progress: halfway results”
09:30h Gopala Krishna Koduri: “Knowledge-based representations for Carnatic music”
09:50h Sankalp Gulati: “Representation and segmentation of melodies in Indian Art Music”
10:10h Ajay Srinivasamurthy: “Rhythm analysis and tāḷa tracking in Carnatic music”
10:30h Coffee break
11:00h Alastair Porter: “Dunya: A system for browsing audio music collections”
11:20h Preeti Rao: “Overview of research done at IIT-Bombay”
11:40h Vedhas Pandit and Kaustuv Kanti Ganguli: “Characterization of melodic motifs”
12:00h T. P. Vinutha: “Rhythmic structure based segmentation”
14:00h Joe Cheri Ross: “Ontology for Indian Music: An Approach for ontology learning from online music forums”
14:20h Amruta J. Vidwans and Prateek Verma: “Melodic style detection in Hindustani music”
14:40h Hema Murthy: “Overview of research done at IIT-Madras”
15:00h Shrey Dutta: “Motif spotting in Carnatic music”
15:20h Coffee break
15:50h P. Sarala and Akshay Anantapadmanabhan: “Cent filterbanks and their relevance to Carnatic music”
16:10h Rajeev Rajan: “Group delay based melody extraction for Indian music”
17:00h End of workshop
Xavier Serra: “CompMusic research progress: halfway results” [slides, video]
In CompMusic we work on computational approaches to analyze and describe music recordings by emphasising the use of domain knowledge of five particular music traditions: Arab-Andalusian (Maghreb), Beijing Opera (China), Turkish-makam (Turkey), Hindustani (North-India) and Carnatic (South-India). Our focus is in describing the melodic and rhythmic characteristic of these musics with the goal to better understand and explore their musical richness and personality. The project started in July 2011 and will continue until June 2016, thus we are at the half way point. An initial effort was the compilation of audio music collections, plus the accompanying information, to be used as the research corpora. Using these collections we have carried out computational analysis on musically relevant aspects such as: tonic, intonation, melodic motives, and rhythmic patterns. Some of the current results have been already integrated into the a newly developed music web browser, Dunya.
Gopala Krishna Koduri: “Knowledge-based representations for Carnatic music” [slides, video]
Efficient data structuring and information management is crucial for retrieving meaningful relationships between various entities in the music. Especially so, when dealing with varied music traditions. Knowledge representation technologies developed in the semantic web context can be utilized to achieve this goal. Further, it will facilitate a knowledge-guided comparison and integration of different models of the same musical concept. But the current development state of ontologies for music is limited in its scope to address these issues. There is a need to develop ontologies for concepts and relationships coming from music theory. In this work, we discuss the melodic concepts that constitute the rāga, the melodic framework of Indian art music, specifically in the context of creating ontologies. We present a first version of the rāga ontology, and discuss the challenges posed by modeling semantics of its substructures. We also present an evaluation strategy that relies on Wikipedia to validate our ontology’s coverage of musical concepts related to rāga.
Sankalp Gulati: “Representation and segmentation of melodies in Indian Art Music” [slides, video]
Melody representation and segmentation play a crucial role in discovery of musically relevant melodic motives and their classification. We discuss a preliminary approach for melody representation, which in addition to fundamental frequency also considers instantaneous loudness and spectral envelope of the lead melodic source. We also discuss a segmentation approach that incorporates domain knowledge and facilitate in identifying melodically stable regions. Furthermore, in the case of Hindustani music some of these melodically stable regions can be identified as nyas segments, which in turn enables efficient melodic motif discovery. We discuss a classification based approach for nyas segment detection that uses musically relevant set of features.
Ajay Srinivasamurthy: “Rhythm analysis and tāḷa tracking in Carnatic music” [slides, video]
Tāḷa tracking is the automatic tracking of the components and events related to the progression of a tāḷa and can provide listeners with a better understanding of the temporal structure of the music piece. Tāḷa tracking from audio music recordings involves an estimation of several useful rhythm descriptors. We present some of the challenges we have faced and discuss the approaches we have explored for estimating the components of the tāḷa in the context of Carnatic music. In specific, we focus on akshara pulse and sama tracking. We also present a rhythm annotated Carnatic music dataset that can be useful to evaluate the algorithms built for different sub-tasks of tāḷa tracking.
Alastair Porter: “Dunya: A system for browsing audio music collections” [slides, video]
Dunya is a web-based software application that lets users interact with an audio music collection through the use of musical concepts that are derived from a specific musical culture. The application includes a database containing information relevant to particular music collections, such as audio recordings, editorial information, and metadata obtained from various sources. An analysis module extracts features from the audio recordings, which are then used to create musically meaningful relationships between all of the items in the database. The application displays the content of these items, allowing users to navigate through the collection by identifying and showing other information that is related to the currently viewed item, either by showing the relationships between them or by using culturally relevant similarity measures. The basic architecture and the design principles developed are reusable for a variety music collections with different characteristics.
To enable better information retrieval for music along with representation content based information, music meta data in textual form are also to be a part of music ontology. This work focussing on information extraction from online sources (rasikas.org,wikipedia) has two primary modules: one to extract the relevant information, such as entities, their properties and relationships from each of the aforementioned sources, and another module to map and interlink such information using our ontologies which will result in a multimodal knowledge-base. Natural language processing (NLP) based approach is adopted for information extraction, analysing syntactic patterns present in the content. Entities are identified performing fuzzy matching with the existing knowledge base and relations are extracted through a rule based approach.
We address the problem of spotting melodic motifs, in an alapana, which are the signature phrases of a raga. This work is based on the previous work where locations of snippets of phrases that exists in the Alapana are identified and then a conventional rough longest common subsequence algorithm is modified to locate the motifs. This work verifies how this approach scales for different ragas.
We present a novel approach for melody extraction based on the modified group delay function as opposed to conventional magnitude based approaches. In the proposed method, the power spectrum of the music signal is first flattened in order to annihilate system characteristics, while emphasising the source characteristics. The flattened magnitude spectrum is treated as a time domain signal. The modified group delay function of this signal produces peaks at multiples of the pitch period. The first three peaks are used to determine the actual pitch period. To address the effects of pitch doubling or halving, a dynamic programming based approach is used. Dynamic variation of pitch is captured by adaptive windowing in which the window size is determined by fixing a static threshold on autocorrelation of Fourier transform magnitude of frames with a lag. Voicing detection is performed using the normalized harmonic energy. The performance of the proposed system was evaluated on North Indian Classical music dataset (MIREX-2008) and Carnatic dataset. The performance is comparable to other magnitude spectrum based approaches. Important feature of the proposed algorithm is that it neither requires any substantial prior knowledge of the structure of musical pitch nor any classification framework.