ISMIR is the world’s leading research forum on processing, searching, organizing and accessing music-related data. MTG's main contributions to ISMIR 2020 are the presentations of 9 papers in the main program, 1 tutorial, and 2 papers in the Late Breaking session. 

The papers presented as part of the main program are: 

• Chandna P., Cuesta H., Gómez E. A Deep Learning Based Analysis-Synthesis Framework For Unison Singing. 

• Cuesta H., McFee B., Gómez E. Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks.

• Gómez-Cañón, J.S., Cano E., Herrera P., Gómez E. Joyful for you and tender for us: the influence of individual characteristics and language on emotion labeling and classification. 

• Petermann D., Chandna P., Cuesta H., Bonada J., Gómez E. Deep Learning Based Source Separation Applied To Choir Ensembles.

• Yesiler, F., Serrà, J., Gómez, E.  Less is more: Faster and better music version identification with embedding distillation. 

• Doras, G., Yesiler, F., Serrà, J., Gómez, E., Peeters, G. Combining musical features for cover detection.

• Correya, A., Bogdanov, D., Joglar-Ongay, L., Serra, X. Essentia.js: A JavaScript Library for Music and Audio Analysis on the Web.

• Ramires, A., Font, F., Bogdanov, D., Smith, J. B. L., Yang, Y.H., Ching, J., Chen, B.Y., Wu, Y.K., Wei-Han, H., Serra, X. The Freesound Loop Dataset and Annotation Tool. 

• Korzeniowski, F., Nieto, O., McCallum, M. C., Won, M., Oramas, S., Schmidt, E. M. Mood classification using listening data.

The tutorial is: 

• Yesiler, F., Tralie, C., Serrà, J.: Version Identification in the 20s

The papers presented in the Late Breaking Session are: 

• Pandrea, A.G., Gómez-Cañón, J.S., Herrera, P. Cross-Dataset Music Emotion Recognition: an End-to-End Approach.

• Alonso-Jiménez, P., Bogdanov, D., Serra, X. Deep embeddings with Essentia models.