3 papers
cs.SD2024
Analyzing and reducing the synthetic-to-real transfer gap in Music Information Retrieval: the task of automatic drum transcription
Mickaël Zehren, Marco Alunno, Paolo Bientinesi
Automatic drum transcription is a critical tool in Music Information Retrieval for extracting and analyzing the rhythm of a music track, but it is limited by the size of the datase…
cs.SD2021
ADTOF: A large dataset of non-synthetic music for automatic drum transcription
Mickael Zehren, Marco Alunno, Paolo Bientinesi
The state-of-the-art methods for drum transcription in the presence of melodic instruments (DTM) are machine learning models trained in a supervised manner, which means that they r…
cs.SD2020
Automatic Detection of Cue Points for DJ Mixing
Mickaël Zehren, Marco Alunno, Paolo Bientinesi
The automatic identification of cue points is a central task in applications as diverse as music thumbnailing, mash-ups generation, and DJ mixing. Our focus lies in electronic danc…