8 citations · 13 across the 6 of their papers we have counts for
6 papers
Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset
Huw Cheston, Jan Van Balen, Simon Durand
Sampling, the practice of reusing recorded music or sounds from another source in a new work, is common in popular music genres like hip-hop and rap. Numerous services have emerged…
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages
Simon Durand, Daniel Stoller, Sebastian Ewert
Lyrics alignment gained considerable attention in recent years. State-of-the-art systems either re-use established speech recognition toolkits, or design end-to-end solutions invol…
Data Cleansing with Contrastive Learning for Vocal Note Event Annotations
Gabriel Meseguer-Brocal, Rachel Bittner, Simon Durand +1
Data cleansing is a well studied strategy for cleaning erroneous labels in datasets, which has not yet been widely adopted in Music Information Retrieval. Previously proposed data…
Dereverberation using joint estimation of dry speech signal and acoustic system
Sanna Wager, Keunwoo Choi, Simon Durand
The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal. In this report, we describe an approach to…
End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
Daniel Stoller, Simon Durand, Sebastian Ewert
Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications. Compared to text-to-…
Robust Downbeat Tracking Using an Ensemble of Convolutional Networks
S. Durand, J. P. Bello, B. David +1
In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized a…