Suchergebnisse

In this paper we describe the data gathering work done within a large research project, CompMusic, which emphasizes a culture specific approach in the automatic description of several world music repertoires. Currently we are focusing on the Hindustani (North India), Carnatic (South India) and Turkish-makam (Turkey) music traditions. The selection and organization of the data to be processed for the characterization of each of these traditions is of the utmost importance. ; The CompMusic project has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) / ERC grant agreement 267583.

From most musical cultures there are digital traces, digital artifacts, that can be processed and studied computationally, and this has been the focus of computational musicology already for several decades. This type of research requires clear formalizations and some simplifications, for example, by considering that a musical culture can be conceptualized as a system of interconnected entities. A musician, an instrument, a performance, or a melodic motive, are examples of entities and they are linked by various types of relationships. We then need adequate digital traces of the entities, for example, a textual description can be a useful trace of a musician and a recording one of a performance. The analytical study of these entities and of their interactions is accomplished by processing the digital traces and by generating mathematical representations and models of them. But a more ambitious goal, however, is to go beyond the study of individual artifacts and analyze the overall system of interconnected entities in order to model a musical culture as a whole. The reader might think that this is science fiction, and he or she might be right, but there is research trying to make advances in this direction. In this article I undertake an overview the state-of-the-art related to this type of research, identifying current challenges, describing computational methodologies being developed, and summarizing musicologically relevant results of such research. In particular, I review the work done within CompMusic, a project in which my colleagues and I have developed audio signal processing, machine learning, and semantic web methodologies to study several musical cultures. ; The CompMusic project has been funded by the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) / ERC grant agreement 267583.

Our information technologies do not respond to the world's/nmulticultural reality; in fact, we are imposing the paradigms/nof our market-driven western culture also on IT, thus facilitating/nthe access of a small part of the world's information/nto a small part of the world's population. The current IT research/nefforts may even make it worse, and future IT will/naccentuate this information bias. Most IT research is being/ncarried out with a western centered approach and as a result,/nmost of our data models, cognition models, user models,/ninteraction models, ontologies, etc., are culturally biased./nThis fact is quite evident in music information research,/nsince, despite the world's richness in terms of musical/nculture, most research is centered on CDs and metadata/nof western commercial music. This is the motivation behind/na large and ambitious project funded by the European Research/nCouncil entitled "CompMusic: Computational Models/nfor the discovery of the world's music." In this paper we/npresent the ideas supporting this project, the challenges that/nwe want to work on, and the proposed approaches to tackle/nthese challenges. ; The CompMusic project has received funding from the/nEuropean Research Council under the European Union's/nSeventh Framework Program (FP7/2007-2013) / ERC grant/nagreement 267583 and will run for five years starting on/nJuly 2011.

A supervised approach to metrical cycle tracking from audio is presented,/nwith a main focus on tracking the tāḷa, the hierarchical cyclic/nmetrical structure in Carnatic music. Given the tāḷa of a piece, we aim/nto estimate the akṣara (lowest metrical pulse), the akṣara period, and/nthe sama (first pulse of the tāḷa cycle). Starting with percussion enhanced/naudio, we estimate the akṣara pulse period from a tempogram/ncomputed using an onset detection function. A novelty function is/ncomputed using a self similarity matrix constructed using frame level/naudio features. These are then used to estimate possible akṣara and/nsama candidates, followed by a candidate selection based on periodicity/nconstraints, which leads to the final estimates. The algorithm/nis tested on an annotated collection of 176 pieces spanning four different/ntāḷas. Though applied to Carnatic music, the framework presented/nis general and can be extended to other music cultures with/ncyclical metrical structures. ; This work is partly supported by the European Research Council under/nthe European Union's Seventh Framework Program, as part of the CompMusic/nproject (ERC grant agreement 267583).

Comunicació presentada a: 5th China Conference on Sound and Music Technology - Chinese Traditional Music Technology Session celebrada el 21 de novembre de 2017 a Suzhou, Xina. ; Music Information Retrieval (MIR) technologies have been proven useful in assisting western classical singing training. Jingju (also known as Beijing or Peking opera) singing is different from western singing in terms of most of the perceptual dimensions, and the trainees are taught by using mouth/heart method. In this paper, we first present the training method used in the professional jingju training classroom scenario and show the potential benefits of introducing the MIR technologies into the training process. The main part of this paper dedicates to identify the potential MIR technologies for jingju singing training. To this intent, we answer the question: how the jingju singing tutors and trainees value the importance of each jingju musical dimension—intonation, rhythm, loudness, tone quality and pronunciation? This is done by (i) classifying the classroom singing practices, tutor's verbal feedbacks into these 5 dimensions, (ii) surveying the trainees. Then, with the help of the music signal analysis, a finer inspection on the classroom practice recording examples reveals the detailed elements in the training process. Finally, based on the above analysis, several potential MIR technologies are identified and would be useful for the jingju singing training. ; This by the European Research Council under the European Union's Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583).

Comunicació presentada al 6th International Workshop on Folk Music Analysis, celebrat els dies 15 a 17 de juny de 2016 a Dublín, Irlanda.Comunicació presentada al 6th International Workshop on Folk Music Analysis, celebrat els dies 15 a 17 de juny de 2016 a Dublín, Irlanda. ; From a computational perspective, structural analysis of OttomanTurkish makam music (OTMM) is a research topic that has not been addressed thoroughly. In this paper we propose a method, which processes machine-readable music scores of OTMM to extract and semiotically describe the melodic and lyrical organization of the music piece automatically using basic string similarity and graph analysis techniques. The proposed method is used to identify around 50000 phrases in 1300 music scores and 21500 sections in 1770 scores, respectively. The obtained information may be useful for relevant research in music education and musicology, and it has already been used to aid several computational tasks such as music score content validation, digital music engraving and audio-score alignment. ; This work is partly supported by the European Research Council under the European Union's Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583).

Comunicació presentada a: Web Audio Conference WAC-2018, celebrat del 19 al 21 de setembre de 2018 a Berlin, Alemanya. ; Recent advancements in web-based audio systems have enabled sufficiently accurate timing control and real-time sound processing capabilities. Numerous specialized music tools, as well as digital audio workstations, are now accessible from browsers. Features such as the large accessibility of data and real-time communication between clients make the web attractive for collaborative data manipulation. However, this innovative field has yet to produce effective tools for multiple-user coordination on specialized music creation tasks. The Multi Web Audio Sequencer is a prototype of an application for segment-based sequencing of Freesound sound clips, with an emphasis on seamless remote collaboration. In this work we consider a fixed-grid step sequencer as a probe for understanding the necessary features of crowd-shared music creation sessions. This manuscript describes the sequencer and the functionalities and types of interactions required for effective and attractive collaboration of remote people during creative music creation activities. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 688382 \AudioCommons".

Comunicació presentada al 1st International workshop on Digial Libraries for Musicology, que va tenir lloc el 12 de setembre de 2014 a Longres, Regne Unit. ; We present a work ow processing and data storage system that has been developed to store the computational analysis of large databases of music-related documents. Documents can consist of any music-related data, for example, audio les or symbolic scores. The system can be used to run fea- ture extraction algorithms to compute new data based on these input documents. The feature extraction process can be distributed over many networked computers to reduce calculation time. Researchers can develop and run their own algorithms on a collection of music-related documents using any programming system without needing system adminis- trator knowledge or direct access to the les, using a web based interface. Computed features are stored in the sys- tem along with a reference to the version of the code that was used to compute it. Audio les and computed features can be accessed over the internet. This system has been developed as a supporting part of the CompMusic project, allowing collaborating researchers to develop new feature ex- traction algorithms and share them with other researchers. It has been released under an open-source licence. ; The CompMusic project is funded by the European Research Council under the European Union's Seventh Framework Program (ERC grant agreement 267583).