Bienvenue sur le site de l'axe IA du laboratoire IBISC

Ce site a pour objectif l'échange d'informations ainsi que l'animation de l'axe de recherche transversal IA du laboratoire.

Voici les prochaines dates des activités de l’axe IA à noter dans nos agendas :

• Seminaire IA : mercredi 1 décembre 2021, 13h30 en présentiel (IBGBI)
  • Geoffroy Peeters, Full-professor at Télécom-Paris, Institut Polytechnique de Paris, IDS (Image-Data-Signal) department, S2A (Signal-Statistique-Apprentissage) team, ADASP (Audio Data Analysis & Signal Processing) group

Title: Deep learning for music audio signal processing

Abstract:As in many fields, deep neural networks have allowed important advances in the processing of musical audio signals. We first present the specificities of these signals and some elements of audio signal processing (as used in the traditional machine-learning approach. We then show how deep neural networks (in particular convolutional neural networks) can be used to perform feature learning. We first recall the fundamental differences between 2D images and time/frequency representations. We then discuss the choice of input (spectrogram, CQT, or raw-waveform), the choice of convolutional filter shape, autoregressive neural models, and the different ways of injecting a priori knowledge (harmonicity, source/filter) into these networks. Finally, we illustrate the different learning paradigms used in the music audio domain: classification, encoder-decoder (source separation, constraints on latent space), metric learning (triplet loss), and semi-supervised learning.

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