ICMPC/ESCOM is a very multidisciplinary conference, bringing together people from very different fields related to music such as music psychology, music perception, neuroscience, music theory, or music information retrieval.
The study investigates several expressive characteristics of unison choir singing, focusing on how singers blend together and interact with each other in terms of fundamental frequency dispersion, intonation, and vibrato. They also present an open dataset of choral singing that is available here, and was created in collaboration with the Anton Bruckner Choir (Barcelona).
This is a picture of the recording session. This work is being carried out in the context of two research projects: CASAS and TROMPA.
A veces los proyectos más enriquecedores son los que realizas con menos recursos.
En éste proyecto utilizamos las tecnologías de recuperación de la información musical para encontrar la banda sonora de la vida de personas mayores españolas. Ésto ha presentado varios retos de investigación y tecnológicos. En concreto hemos podido observar el sesgo de los sistemas actuales de recomendación musical tanto en que sólo se centran en un repertorio musical popular y sus interfaces y descriptores musicales están pensados para usuarios jóvenes. Esto aplica a:
Las taxonomías de géneros musicales.
Los idiomas predominantes en las canciones y su etiquetado.
Los descriptores y playlists que se utilizan (e.g. música de fiesta).
La dificultad para tener los derechos de dar una canción a éstas personas para que la escuchen.
La dificultad de tener dispositivos fáciles de usar y dirigidos a éste tipo de personas.
Atención! El sistema está pensado para personas mayores que han nacido o viven en España, por lo que utilizadlo sobre todo si cumplís éstos requisitos o para alguna persona que conozcáis que los cumpla.
Ha sido un privilegio poder colaborar con la Fundación Pasqual Maragall, La Fundación AVAN y la Escuela La Salud de Sabadell en éste piloto con personas que padecen la enfermedad de Alzheimer. Sobre todo ha sido genial trabajar con Nina, Anna, Carolina, y los chicos de La Salut. Aquí podéis ver un vídeo que me encanta y resume muy bien el proyecto:
Para más información no os perdáis el documental Sense Ficció que emitirá TV3 el día 8 de Mayo por la noche!!!
¡Gracias a todo el equipo! En especial al grupo del MTG: Perfe Herrera, Felipe Navarro, Olga Slizovskaia, por su tiempo en éste proyecto donde no hemos tenido financiación específica.
Sometimes the most enriching projects are those that you do with less resources and funding.
In this project we use music information retrieval (MIR) technologies to find the life soundtrack of Spanish elderly people. Starting from a questionnaire where we ask about biographical information and one´s relationship with music, we build a playlist looking at several sources such as spotify or youtube.
This has presented several research and technological challenges. Specifically, we have been able to observe the bias of current music recommendation systems, as they focus on a popular musical repertoire and their interfaces and musical descriptors are designed for young users. This applies to:
The taxonomies of musical genres.
The predominant languages in the songs and their labeling.
The descriptors and playlists that are used (e.g., what does happy music mean).
The difficulty to have the rights to give a song to these people to be heard.
The difficulty of having easy-to-use devices aimed at this type of person.
And if someone wants to get their soundtrack you can do it at bandasonoravital.upf.edu Attention! The system is designed for seniors who have been born or live in Spain, so use it especially if you meet these requirements or for someone you know who complies.
It has been a privilege to be able to collaborate with the Pasqual Maragall Foundation, the AVAN Foundation and the Health School of Sabadell in this pilot with people suffering from Alzheimer’s disease. Above all it has been great to work with Nina, Anna, Carolina, and the guys from La Salut. Here you can see a video that I love and sums up the project very well:
For more information, do not miss the Sense Ficció documentary that will broadcast TV3 on May 8 at night !!!
More information on the project on the MTG website and the Pasqual Maragall Foundation. Thanks to all the team! Especially to the MTG group: Perfe Herrera, Felipe Navarro, Olga Slizovskaia, for their time in this project where we have not had specific funding.
The March Issue (Vol.10, Issue 1) of ACMSIGMMRecords (News for the Multimedia Community) is out and it includes an interview of myself for the interdisciplinary column, kindly chaired by Cynthia Liem and Jochen Huber.
It is awesome already that there is an interdisciplinary column at SIGMM, recognising the challenges and also the potential of interdisciplinary research and insights as a way to have a comprehensive understanding, in this case, of multimedia computing. I was very pleased to ask questions about my experience in the MIR field and about diversity and interdisciplinarity.
You can read the interview and other interesting content here.
The HUMAINT project will (1) provide a scientific understanding of machine vs human intelligence; (2) analyze the influence of machine learning algorithms into human behaviour (3) investigate to what extent these findings should influence the European regulatory framework. Given my research expertise, music will be an important use case to address.
In the context of this project, three postdoc positions in the area of machine learning and human behaviour are open for appointment from January 1, 2018, at the Joint Research Centre (European Commission) in Seville, Spain. The fully funded positions are available for a period of three years. Particular areas of interests:
Fairness, accountability, transparency, explainability of machine learning methods.
Social, ethical and economic aspects of artificial intelligence.
Human-computer interaction and human-centered machine learning.
Digital and behavioural economy.
Application domains: music and arts, social networks, health, transport, energy.
We are looking for highly motivated, independent, and outstanding postdoc candidates with a strong background in machine learning and/or human behaviour. An excellent research track record, ability to communicate research results and involvement in community initiatives is expected. Candidates should have EU/EEA citizenship.
The JRC offers an enriching multi-cultural and multi-lingual work environment with lifelong learning and professional development opportunities, and close links to top research organisations and international bodies around the world. Postdoctoral researchers receive a competitive salary and excellent working conditions, and will define their own research agenda inline with the project goals.
JRC-Seville is located in Cartuja 93 scientific and technological park. Seville is the fourth-largest city in Spain. With more than 30 centuries of history (gateway of America for two centuries, main actor in the first circumnavigation of the Earth), three UNESCO World Heritage Sites, and privileged climate, it combines its historical and touristic character with a consolidated economic development and innovation potential.
We are also open for collaborations with external researchers, as one of our goals is to build an expert network in the topics.
I have been collaborating for a while now on the edition of a Special Issue at IEEE Multimedia Magazine, which gathers state-of-the-art research on multimedia methods and technologies aimed at enriching music performance, production and consumption.
I have had the change to co-edit this issue with my colleagues Cynthia Liem (TU Delft, The Netherlands) and George Tzanetakis (University of Victoria, Canada), and I am very happy with the outcomes.
It is the second time I act as a co-editor for a journal (the first one was at JNMR and related to computational ethnomusicology) and I learnt a lot from the process. Editors have to asure good submissions, good reviews and recommendations, keeping the coherence and theme that we wanted to give as a message to our community. Yes: access, distribution and experiences in music are changing with new technologies. I am very happy with the outcomes! Check our editorial paper here, and the full issuehere.
As part of the PHENICX project, we have recently published our research results in the task of audio sound source separation, which is the main research topic of one of our PhD students, Marius Miron.
During this work, we developed a method for orchestral music source separation along with a new dataset: the PHENICX-Anechoic dataset. The methods were integrated into the PHENICX project for tasks as orchestra focus/instrument enhancement. To our knowledge, this is the first time source separation is objectively evaluated in such a complex scenario.
Abstract: This paper proposes a system for score-informed audio source separation for multichannel orchestral recordings. The orchestral music repertoire relies on the existence of scores. Thus, a reliable separation requires a good alignment of the score with the audio of the performance. To that extent, automatic score alignment methods are reliable when allowing a tolerance window around the actual onset and offset. Moreover, several factors increase the difficulty of our task: a high reverberant image, large ensembles having rich polyphony, and a large variety of instruments recorded within a distant-microphone setup. To solve these problems, we design context-specific methods such as the refinement of score-following output in order to obtain a more precise alignment. Moreover, we extend a close-microphone separation framework to deal with the distant-microphone orchestral recordings. Then, we propose the first open evaluation dataset in this musical context, including annotations of the notes played by multiple instruments from an orchestral ensemble. The evaluation aims at analyzing the interactions of important parts of the separation framework on the quality of separation. Results show that we are able to align the original score with the audio of the performance and separate the sources corresponding to the instrument sections.
The PHENICX-Anechoic dataset includes audio and annotations useful for different MIR tasks as score-informed source separation, score following, multi-pitch estimation, transcription or instrument detection, in the context of symphonic music. This dataset is based on the anechoic recordings described in this paper:
Pätynen, J., Pulkki, V., and Lokki, T., “Anechoic recording system for symphony orchestra,” Acta Acustica united with Acustica, vol. 94, nr. 6, pp. 856-865, November/December 2008.
Humans use singing to create identity, express emotion, tell stories, exercise creativity, and connect with each other while singing together. This is demonstrated by the large community of music singers active in choirs and the fact that vocal music makes up an important part of our cultural heritage. Currently, an increasing amount of music resources are becoming digital, and the Web has become an important tool for singers to discover and study music, as a feedback resource and as a way to share their singing performances. The CASAS project has two complementary goals:
The first one is to improve state-of-the-art technologies that assist singers in their musical practice. We research on algorithms for singing analysis and synthesis (ex: automatic transcription, description, synthesis, classification and visualization), following a user-centered perspective, and with the goal of making them more robust, scalable and musically meaningful.
The second one is to enhance current public-domain vocal music archives and create research data for our target music information retrieval (MIR) tasks. Our project put a special emphasis on choral repertoire in Catalan and Spanish.
Our paper on melodic similarity is finally online! The paper is titled
Melodic Contour and Mid-Level Global Features Applied to the Analysis of Flamenco Cantes
This work focuses on the topic of melodic characterization and similarity in a specific musical repertoire: a cappella flamenco singing, more specifically in debla and martinete styles. We propose the combination of manual and automatic description. First, we use a state-of-the-art automatic transcription method to account for general melodic similarity from music recordings. Second, we define a specific set of representative mid-level melodic features, which are manually labelled by flamenco experts. Both approaches are then contrasted and combined into a global similarity measure. This similarity measure is assessed by inspecting the clusters obtained through phylogenetic algorithms and by relating similarity to categorization in terms of style. Finally, we discuss the advantage of combining automatic and expert annotations as well as the need to include repertoire-specific descriptions for meaningful melodic characterization in traditional music collections.
This is the result of a joint work of the COFLA group, where I am contributing with tecnologies for the automatic transcription and melody description of music recordings.
This is an example on how we compare flamenco tonás using melodic similarity and phylogenetic trees:
And this is a video example of the type of styles we analyze in this paper, done by Nadine Kroher based on her work at the MTG: