22 citations · 93 across the 11 of their papers we have counts for
14 papers
Video-to-Music Recommendation using Temporal Alignment of Segments
Laure Prétet, Gaël Richard, Clément Souchier +1
We study cross-modal recommendation of music tracks to be used as soundtracks for videos. This problem is known as the music supervision task. We build on a self-supervised system…
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…
Is there a "language of music-video clips" ? A qualitative and quantitative study
Laure Prétet, Gaël Richard, Geoffroy Peeters
Recommending automatically a video given a music or a music given a video has become an important asset for the audiovisual industry - with user-generated or professional content.…
Cross-Modal Music-Video Recommendation: A Study of Design Choices
Laure Pretet, Gael Richard, Geoffroy Peeters
In this work, we study music/video cross-modal recommendation, i.e. recommending a music track for a video or vice versa. We rely on a self-supervised learning paradigm to learn fr…
Content based singing voice source separation via strong conditioning using aligned phonemes
Gabriel Meseguer-Brocal, Geoffroy Peeters
Informed source separation has recently gained renewed interest with the introduction of neural networks and the availability of large multitrack datasets containing both the mixtu…
Learning to rank music tracks using triplet loss
Laure Prétet, Gaël Richard, Geoffroy Peeters
Most music streaming services rely on automatic recommendation algorithms to exploit their large music catalogs. These algorithms aim at retrieving a ranked list of music tracks ba…
A Prototypical Triplet Loss for Cover Detection
Guillaume Doras, Geoffroy Peeters
Automatic cover detection -- the task of finding in a audio dataset all covers of a query track -- has long been a challenging theoretical problem in MIR community. It also became…
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…
Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations
Gabriel Meseguer-Brocal, Geoffroy Peeters
Data-driven models for audio source separation such as U-Net or Wave-U-Net are usually models dedicated to and specifically trained for a single task, e.g. a particular instrument…
DALI: a large Dataset of synchronized Audio, LyrIcs and notes, automatically created using teacher-student machine learning paradigm
Gabriel Meseguer-Brocal, Alice Cohen-Hadria, Geoffroy Peeters
The goal of this paper is twofold. First, we introduce DALI, a large and rich multimodal dataset containing 5358 audio tracks with their time-aligned vocal melody notes and lyrics…
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…