Pons J, Nieto O, Prockup M, Schmidt EM, Ehmann AF, Serra X. End-to-end learning for music audio tagging at scale. In the workshop on Machine Learning for Audio Signal Processing (ML4Audio), NIPS.
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.
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Pons J, Nieto O, Prockup M, Schmidt EM, Ehmann AF, Serra X. End-to-end learning for music audio tagging at scale. In the workshop on Machine Learning for Audio Signal Processing (ML4Audio), NIPS.
Pons J, Nieto O, Prockup M, Schmidt EM, Ehmann AF, Serra X. End-to-end learning for music audio tagging at scale. In the workshop on Machine Learning for Audio Signal Processing (ML4Audio), NIPS.
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 for 1.2 million tracks. This large amount of data allows us to unrestrictedly explore different front-end paradigms: from assumption-free models - using waveforms as input with very small convolutional filters; to models that rely on domain knowledge - log-mel spectrograms with a convolutional neural network designed to learn temporal and timbral features. Results suggest that while spectrogram-based models surpass their waveform-based counterparts, the difference in performance shrinks as more data are employed.
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