The Music Enthusiasts platform aims to improve the gathering and study of the so-called “ground truth” data for machine learning models of emotions in music. We offer a citizen science approach with two objectives: i) provide learning resources for our participants on general notions of musical properties and their relation to emotions, and ii) incentivize engagement with our platform by rewarding users with music recommendations. In a nutshell, our annotation tool targets enhancing interdisciplinary discussions regarding music emotion annotation and analysis.
In the frame of the TROMPA project, the Music Information Research Lab of the MTG is conducting a study on the impact of music recommendation algorithms, particularly on diversity/variety of the listening experience.
It takes 25-35 minutes to complete the survey, depending on your familiarity with electronic music, and at the end of the survey you get a playlist with all the tracks that are used during the survey. Please follow the instructions carefully to guarantee your comprehension, correctness, and overall enjoyment!
Music can express and convey many emotions, which relate to certain musical features. We want you to learn different musical attributes that relate music to an expressed emotion. From 29th june to 5th july 2020, use our application and annotate music from all around the world. By the end of the week, we will give prizes to the participants that contribute the most.
Today we have released the Music Enthusiasts use case working pilot to be tested during the following weeks! We are currently running online testing with real users to validate the functionalities of the tool.
During the last six months, we've focused on refining the requirements and goals of the pilot to attract more contributors. We've done multiple workshops to evaluate the usability and users' perception of different demos.These workshops allowed us to refine the workflow of the application, and to include new features to engage users.
The Workshop-Symposium on Methods in Music and Emotion took place on the 14th of September 2019 at St. Chad’s College, Durham University, United Kingdom. Participants gave presentations focusing on research methodology deployed in experiments on the topic of music and emotion, with a primary interest in cross-cultural applications. Juan Sebastián Gómez Cañón (UPF) presented his work on emotion annotation analysis done in collaboration with Emilia Gómez (UPF/JRC), Perfecto Herrera (UPF), and Estefanía Cano (A*STAR).
The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. As RecSys brings together the main international research groups working on recommender systems, along with many of the world’s leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender systems research.
The International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) is an annual conference on computational linguistics (CL) and natural language processing (NLP). The conference is intended to encourage exchange of opinions between scientists working in different areas related to computational linguistics, intelligent text and speech processing.
Do you like music, although you are not that experienced regarding classical music? Many people for whom this is the case do not know where to start, and what to listen or look for.
Within TROMPA, we will work on more accessible rich entrances to this music repertoire, and on ways to engage you from the start in unlocking this repertoire, even if you would not have specialist musical knowledge.
Keep watching this page for future updates, and get involved!