22 citations · 93 across the 11 of their papers we have counts for
6 papers · 1 filter
SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss
Geoffroy Peeters, Florian Angulo
In this paper, we propose a new paradigm to learn audio features for Music Structure Analysis (MSA). We train a deep encoder to learn features such that the Self-Similarity-Matrix…
Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts
Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters +1
As music has become more available especially on music streaming platforms, people have started to have distinct preferences to fit to their varying listening situations, also know…
Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation
Christof Weiß, Geoffroy Peeters
Extracting pitch information from music recordings is a challenging but important problem in music signal processing. Frame-wise transcription or multi-pitch estimation aims for de…
Cover Detection using Dominant Melody Embeddings
Guillaume Doras, Geoffroy Peeters
Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in th…
Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation
Alice Cohen-Hadria, Axel Roebel, Geoffroy Peeters
State-of-the-art singing voice separation is based on deep learning making use of CNN structures with skip connections (like U-net model, Wave-U-Net model, or MSDENSELSTM). A key t…
Single-Channel Blind Source Separation for Singing Voice Detection: A Comparative Study
Dominique Fourer, Geoffroy Peeters
We propose a novel unsupervised singing voice detection method which use single-channel Blind Audio Source Separation (BASS) algorithm as a preliminary step. To reach this goal, we…