Browsing by Author "Zinemanas, Pablo"

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  • An Interpretable deep learning model for automatic sound classification
    Zinemanas, Pablo; Rocamora, Martín; Miron, Marius; Font Corbera, Frederic; Serra, Xavier (MDPI, 2021)
    Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may ...
  • DCASE-models: a Python library for computational environmental sound analysis using deep-learning models
    Zinemanas, Pablo; Hounie, Ignacio; Cancela, Pablo; Font Corbera, Frederic; Rocamora, Martín; Serra, Xavier (Detection and Classication of Acoustic Scenes and Events (DCASE), 2020)
    This document presents DCASE-models, an open–source Python library for rapid prototyping of environmental sound analysis systems, with an emphasis on deep–learning models. Together with a collection of functions for ...
  • Leveraging pre-trained autoencoders for interpretable prototype learning of music audio
    Alonso Jiménez, Pablo; Pepino, Leonardo; Batlle-Roca, Roser; Zinemanas, Pablo; Bogdanov, Dmitry; Serra, Xavier; Rocamora, Martín (Institute of Electrical and Electronics Engineers (IEEE), 2024)
    We present PECMAE an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, ...
  • Toward interpretable polyphonic sound event detection with attention maps based on local prototypes
    Zinemanas, Pablo; Rocamora, Martín; Fonseca, Eduardo; Font, Frederic; Serra, Xavier (Universitat Pompeu Fabra. Music Technology Group, 2021)
    Understanding the reasons behind the predictions of deep neural networks is a pressing concern as it can be critical in several application scenarios. In this work, we present a novel interpretable model for polyphonic ...
  • Urban sound & sight: dataset and benchmark for audio-visual urban scene understanding
    Fuentes, Magdalena; Steers, Bea; Zinemanas, Pablo; Rocamora, Martín; Bondi, Luca; Wilkins, Julia; Shi, Qianyi; Hou, Yao; Das, Samarjit; Serra, Xavier; Bello, Juan Pablo (Institute of Electrical and Electronics Engineers (IEEE), 2022)
    Automatic audio-visual urban traffic understanding is a growing area of research with many potential applications of value to industry, academia, and the public sector. Yet, the lack of well-curated resources for training ...
  • Visual music transcription of clarinet video recordings trained with audio-based labelled data
    Gómez Gutiérrez, Emilia, 1975-; Arias Martínez, Pablo; Zinemanas, Pablo; Haro Ortega, Gloria (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    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 ...

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