Last week we announced the publication of OpenBMAT, an open dataset for the tasks of music detection and relative music loudness estimation. The dataset contains 27.4 hours of audio from 8 different TV program types at 4 different countries, cross-annotated by 3 people using 6 different classes. It has been published as a dataset paper at Transaction of the International Society for Music Information Retrieval, the open journal of ISMIR. This research has been carried out as a collaboration between the MTG and BMAT in the context of the industrial Doctorates program of the Catalan Government.
For more information you can read the related news at MTG web site: https://www.upf.edu/web/mtg/home/-/asset_publisher/sWCQhjdDLWwE/content/id/227864284/maximized#.XXZ_IZMzab8
Two PhD students, Blai Meléndez-Català and Andrés Pérez-López, are joining my lab thanks to the industrial doctorate program from AGAUR, which supports collaboration between universities and industrial partners, in this case both from Barcelona. These students will work at the company but come to the lab for some time to interact and collaborate with us.
I will be the main academic supervisor of these projects, which are both linked to our research on audio processing and description, and dealing with large audio datasets and focusing on two particular problems:
- “Music/Speech Detection in Broadcast Media Programs” in collaboration with BMAT, in particular with Emilio Molina. Blai Meléndez Català is our PhD fellow, and the goal of this project is to research on the task of audio segmentation and tagging in the context audiovisual recordings.
- “Immersive Audiovisual Production Enhacement based on 3D Audio“, in collaboration with Fundación Eurecat, in particular with the audio-visual technologies group leaded by Adan Garriga. This project is related to 3D audio for virtual reality applications, and Andrés Pérez is a new PhD student that will research on innovative production tools for creative industries.
There is some more info (in catalan or spanish) on the UPF web site.
Our review article on melody extraction algorithms for the IEEE Signal Processing Magazine is finally available online! The printed edition will be coming out in March 2014.
I believe (not just as I am a co-author!) that it will become a key reference in the Music Information Retrieval area and beyond, as it provides a very nice overview of approaches, challenges and applications for melody extraction from polyphonic music signals. Justin Salamon has been the main author (congratulations, Justin!) and the paper has benefit from the contribution of two key experts: Gaël Richard, and Dan Ellis, with who I had the chance to collaborate on a previous comparative study on melody extraction published at IEEE TASP (128 citations according to google scholar).
Finally, I like very much this kind of tutorial papers providing a comprehensive introduction to a given topic and with a very attractive design. I hope you will enjoy it!
J. Salamon, E. Gómez, D. P. W. Ellis and G. Richard, “Melody Extraction from Polyphonic Music Signals: Approaches, Applications and Challenges“, IEEE Signal Processing Magazine, 31(2):118-134, Mar. 2014.
Abstract—Melody extraction algorithms aim to produce a sequence of frequency values corresponding to the pitch of the dominant melody from a musical recording. Over the past decade melody extraction has emerged as an active research topic, comprising a large variety of proposed algorithms spanning a wide range of techniques. This article provides an overview of these techniques, the applications for which melody extraction is useful, and the challenges that remain. We start with a discussion of ‘melody’ from both musical and signal processing perspectives, and provide a case study which interprets the output of a melody extraction algorithm for specific excerpts. We then provide a comprehensive comparative analysis of melody extraction algorithms based on the results of an international evaluation campaign. We discuss issues of algorithm design, evaluation and applications which build upon melody extraction. Finally, we discuss some of the remaining challenges in melody extraction research in terms of algorithmic performance, development, and evaluation methodology.
For further information about this article please visit Justin Salamon’s research page.