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 [MSc thesis] Audio Data Augmentation with respect to Musical Instrument Recognition

[MSc thesis] Audio Data Augmentation with respect to Musical Instrument Recognition

Author: Siddharth Bhardwaj

Supervisors: Olga Slizovskaia, Emilia Gómez and Gloria Haro

MSc program: Master in Sound and Music Computing

Identifying musical instruments in a polyphonic music recording is a difficult yet crucial problem in music information retrieval. It helps in auto-tagging of a musical piece by instrument, consequently enabling searching music databases by instrument. Other useful applications of instrument recognition are source separation, genre recognition, music transcription, and instrument specific equalizations. We review the state of the art methods for the task, including the recent Convolutional Neural Networks based approaches. These deep learning models require large quantities of annotated data, a problem which can be partly solved by synthetic data augmentation. We study different types of audio data transformations that can help in various audio related tasks, publishing an augmentation library in the process. We investigate the effect of using augmented data during the training process of three state of the art CNN based models. We achieved a performance improvement of 2% over the best performing model with almost half the number of trainable model parameters. We attained 6% performance improvement for the single-layer CNN architecture, and 4% for the multi-layer architecture . Also, we study the influence of each type of audio augmentation on each instrument class individually.

Additional material: