F
Francesco Piccoli
Publications - 3
Citations - 84
Francesco Piccoli is an academic researcher. The author has contributed to research in topics: Deep learning & Audio signal. The author has an hindex of 3, co-authored 3 publications receiving 63 citations.
Papers
More filters
Posted Content
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
TL;DR: This work reproduces the implementation of traditional feature engineering based approaches and proposes a new model based on deep learning that outperforms classical models on the arousal detection task, and shows that both approaches perform equally on the valence prediction task.
WASABI: a Two Million Song Database Project with Audio and Cultural Metadata plus WebAudio enhanced Client Applications
Gabriel Meseguer-Brocal,Geoffroy Peeters,Guillaume Pellerin,Michel Buffa,Elena Cabrio,Catherine Faron Zucker,Alain Giboin,Isabelle Mirbel,Romain Hennequin,Manuel Moussallam,Francesco Piccoli,Thomas Fillon +11 more
TL;DR: This paper presents the WASABI project, started in 2017, which aims at the construction of a 2 million song knowledge base that combines metadata collected from music databases on the Web, metadata resulting from the analysis of song lyrics, and metadata result from the audio analysis.
Proceedings ArticleDOI
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
TL;DR: In this paper, the authors proposed a deep learning model for multimodal music mood prediction based on the audio signal and the lyrics of a track, and compared the performance of both approaches on a database containing 18,000 tracks with associated valence and arousal values and show that their approach outperforms classical models on the arousal detection task, and that both approaches perform equally on the valence prediction task.