List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).

List of publications acknowledging the funding in Scopus.

The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository   as well as at the UPF e-repository   



Back Zinemanas et al., Visual music transcription of clarinet video recordings trained with audio-based labelled data. CVAVM 2017.

Zinemanas P, Arias P, Haro G, Gómez E. Visual music transcription of clarinet video recordings trained with audio-based labelled data. ICCV  2017 Workshop on Computer Vision for Audio-Visual Media (CVAVM)


Automatic transcription is a well-known task in the music information retrieval (MIR) domain, and consists on the computation of a symbolic music representation (e.g. MIDI) from an audio recording. In this work, we address the automatic transcription of video recordings when the audio modality is missing or it does not have enough quality, and thus analyze the visual information. We focus on the clarinet which is played by opening/closing a set of holes and keys. We propose a method for automatic visual note estimation by detecting the fingertips of the player and measuring their displacement with respect to the holes and keys of the clarinet. To this aim, we track the clarinet and determine its position on every frame. The relative positions of the fingertips are used as features of a machine learning algorithm trained for note pitch classification. For that purpose, a dataset is built in a semiautomatic way by estimating pitch information from audio signals in an existing collection of 4.5 hours of video recordings from six different songs performed by nine different players. Our results confirm the difficulty of performing visual vs audio automatic transcription mainly due to motion blur and occlusions that cannot be solved with a single view.

Additional material: