• Training neural audio classifiers with few data

  Pons Puig, Jordi; Serrà Julià, Joan; Serra, Xavier (Institute of Electrical and Electronics Engineers (IEEE), 2019)

  We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections. In particular, we study whether (i) a naive regularization of the solution space, ...
• End-to-end sound source separation conditioned on instrument labels

  Slizovskaia, Olga; Kim, Leo; Haro Ortega, Gloria; Gómez Gutiérrez, Emilia, 1975- (Institute of Electrical and Electronics Engineers (IEEE), 2019)

  Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-UNet [1] model which allows end-to-end monaural ...
• The Few-get-richer: a surprising consequence of popularity-based rankings

  Germano, Fabrizio; Gómez, Vicenç; Le Mens, Gaël (ACM Association for Computer Machinery, 2019)

  Ranking algorithms play a crucial role in online platforms ranging from search engines to recommender systems. In this paper, we identify a surprising consequence of popularity-based rankings: the fewer the items reporting ...
• Interactive discovery system for direct democracy

  Aragón, Pablo; Bermejo, Yago; Gómez, Vicenç; Kaltenbrunner, Andreas (Institute of Electrical and Electronics Engineers (IEEE), 2018)

  Decide Madrid is the civic technology of Madrid City Council which allows users to create and support online petitions. Despite the initial success, the platform is encountering problems with the growth of petition signing ...
• 3D reconstruction of both shape and bone mineral density distribution of the femur from DXA images

  Humbert, Ludovic; Whitmarsh, Tristan; De Craene, Mathieu; del Río Barquero, Luis Miguel; Fritscher, Karl; Schubert, Rainer; Eckstein, Felix; Link, Thomas; Frangi Caregnato, Alejandro (Institute of Electrical and Electronics Engineers (IEEE), 2010)
• 3D bone mineral density distribution and shape reconstruction of the proximal femur from a single simulated DXA image: an in vitro study

  Whitmarsh, Tristan; Humbert, Ludovic; De Craene, Mathieu; del Río Barquero, Luis Miguel; Fritscher, Karl; Schubert, Rainer; Eckstein, Felix; Link, Thomas; Frangi Caregnato, Alejandro (Society of Photo Optical Instrumentation Engineers (SPIE), 2010)

  Area Bone Mineral Density (aBMD) measured by Dual-energy X-ray Absorptiometry (DXA) is an established criterion in the evaluation of hip fracture risk. The evaluation from these planar images, however, is limited to 2D ...
• Supporting the planning of hybrid-MOOCs learning designs

  Albó, Laia; Butera Castelo, Rodrigo; Hernández Leo, Davinia (CEUR Workshop Proceedings, 2019)

  This paper presents a work-in-progress solution for planning hybrid Massive Open Online Courses (MOOC). The use of MOOCs in brick and mortar courses presents several design challenges. One of them ...
• End­-to-­end learning for music audio tagging at scale

  Pons Puig, Jordi; Nieto, Oriol; Prockup, Matthew; Schmidt, Erik M.; Ehmann, Andreas F.; Serra, Xavier (2017)

  The lack of data tends to limit the outcomes of deep learning research – specially, when dealing with end-to-end learning stacks processing raw data such as waveforms. In this study we make use of musical labels annotated ...
• Reasoning about policy behavior in logic-based trust management systems: some complexity results and an operational framework

  Pasarella Sánchez, Ana Edelmira; Lobo, Jorge (Institute of Electrical and Electronics Engineers (IEEE), 2015)

  In this paper we show that the logical framework proposed by Becker et al. [1] to reason about security policy behavior in a trust management context can be captured by an operational framework that is based on the language ...
• Correspondence between audio and visual deep models for musical instrument detection in video recordings

  Slizovskaia, Olga; Gómez Gutiérrez, Emilia, 1975-; Haro Ortega, Gloria (International Society for Music Information Retrieval (ISMIR), 2017)

  This work aims at investigating cross-modal connections between audio and video sources in the task of musical instrument recognition. We also address in this work the understanding of the representations learned by ...
• Creating an a cappella singing audio dataset for automatic Jingju singing evaluation research

  Gong, Rong; Caro Repetto, Rafael; Serra, Xavier (ACM Association for Computer Machinery, 2017)

  e data-driven computational research on automatic jingju (also known as Beijing or Peking opera) singing evaluation lacks a suitable and comprehensive a cappella singing audio dataset. In this work, we present an a ...
• Sing Master: an intelligent mobile game for teaching singing

  Dzhambazov, Georgi Bogomilov; Goranchev, Kamen (Institute of Electrical and Electronics Engineers (IEEE), 2016)

  In this paper we present Sing Master - a new mobile application that allows singing enthusiasts to fulfil their dream: to learn singing. Sing Master is a game-like singing tutor that presents a series of singing exercises ...
• Shall we learn together in loud spaces? Towards understanding the effects of sound in collaborative learning environments

  Vujovic, Milica; Hernández Leo, Davinia (2019)

  In this paper we question the role of environmental sound on the process of CL. The first pilot study is presented where we investigated effects of environmental sound on EDA and voice VA of the participants. The created ...
• Flexible CSCL orchestration technology: mechanisms for elasticity and dynamism in pyramid script flows

  Manathunga, Kalpani; Hernández Leo, Davinia (2019)

  Flow patterns (e.g., Pyramid or Snowball) formulate good practices to script collaborative learning scenarios, which have been experimented in small-scale settings widely. Applying flow patterns on large-scale contexts ...
• Using offline metrics and user behavior analysis to combine multiple systems for music recommendation

  Ferraro, Andrés; Bogdanov, Dmitry; Choi, Kyumin; Serra, Xavier (2018)

  There are many offline metrics that can be used as a reference for evaluation and optimization of the performance of recommender systems. Hybrid recommendation approaches are commonly used to improve some of those metrics ...
• End-to-end learning for music audio tagging at scale

  Pons Puig, Jordi; Nieto, Oriol; Prockup, Matthew; Schmidt, Erik M.; Ehmann, Andreas F.; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2018)

  The lack of data tends to limit the outcomes of deep learning research, particularly when dealing with end-to-end learning stacks processing raw data such as waveforms. In this study, 1.2M tracks annotated with musical ...
• Phrase-level modeling of expression in violin performances

  Ortega, Fabio J. M.; Giraldo, Sergio; Ramírez, Rafael,1966- (Machine Learning and Music (MML), 2017)

  A model is proposed for predicting expressive variations in dynamics for violin performances with the purpose of facilitating expressive performance learning by students. The model uses phrases rather than single notes ...
• Automatic playlist continuation using a hybrid recommender system combining features from text and audio

  Ferraro, Andrés; Bogdanov, Dmitry; Yoon, Jisang; Kim, KwangSeob; Serra, Xavier (ACM Association for Computer Machinery, 2018)

  The ACM RecSys Challenge 2018 focuses on music recommendation in the context of automatic playlist continuation. In this paper, we describe our approach to the problem and the final hybrid system that was submitted to the ...
• Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions

  Gong, Rong; Serra, Xavier (International Speech Communication Association (ISCA), 2018)

  In this paper, we tackle the singing voice phoneme segmentation problem in the singing training scenario by using language independent information – onset and prior coarse duration. We propose a two-step method. In the ...
• Bowing modeling for violin students assistance

  Ortega, Fabio J. M.; Giraldo, Sergio; Ramírez, Rafael,1966- (ACM Association for Computer Machinery, 2017)

  Though musicians tend to agree on the importance of practicing expressivity in performance, not many tools and techniques are available for the task. A machine learning model is proposed for predicting bowing velocity ...

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