Audio Signal Processing Lab
|Xavier Serra, Faculty, head of lab||Georgi Dzhambazov, PhD student|
|Dmitry Bogdanov, Postdoc||Gong Rong, PhD student|
|Frederic Font, Postdoc||Alastair Porter, researcher|
|Gopala Krishna Koduri, PhD student||Oriol Romani, researcher|
|Sertan Şentürk, PhD student||Andrés Ferraro, researcher|
|Sankalp Gulati, Postdoc||Hasan Sercan Atlı, researcher|
|Jordi Pons, PhD student||Swapnil Gupta, researcher|
|Rafael Caro Repetto, PhD student||Eduardo Fonseca, PhD student|
|Sergio Oramas, PhD student||Xavier Favory, PhD student|
Current focus of the Audio Signal Processing Lab of the MTG is to combine audio signal processing methods with machine learning and semantic technologies in order to create large and structured sound and music collections and to extract useful musical knowledge from them. Our current research is partly supported through several EU projects (CompMusic, AudioCommons, CAMUT) and national projects (MINGUS, DTIC-MdM).
In the context of CompMusic we are interested in the development of music description techniques through the study of the art music traditions of India (Hindustani and Carnatic), Turkey (Turkish-makam), Maghreb (Arab-Andalusian), and China (Beijing Opera) (ex: Serra 2012; Serra, 2011). Our approach is based on combining signal-processing and machine-learning methodologies and thus a big effort has been dedicated to put together appropriate research corpora with which to carry this data-driven work. Using these corpora we have been focusing on the study on melodic and rhythmic issues with the goal to identify musically meaningful patterns and develop similarity measures between the relevant data entities. This work is permitting us to develop Dunya, which integrates the music corpora and software tools with which to browse them.
Most of the core signal processing algorithms being developed and used in our research projects are part of Essentia, an open-source C++ library for audio processing optimised for scalability (Bogdanov et al., 2013).
Since 2005 we have been developing and maintaining Freesound.org, a platform with which we do research on social computing and semantic web topics (ex: Font , 2015; Roma et al., 2012). Freesound is an excellent platform in which we have been experimenting, deploying and evaluating research ideas related to audio description, classification, recommendation, similarity measures, tag propagation and ontologies.
More recently we are actively involved in the development of Acousticbrainz.org, an open platform for crowdsourcing audio analysis data of commercial music recordings, obtained using Essentia, that can be of use for a variety of music information research and application tasks.