- |
- |
- |
as Télécom Paris (see IRCAM web page for others) |
- |
- |
2024 |
Conference |
R. Mignot, G. Peeters |
Invariant Audio Prints for Music Indexing and Alignment |
In CBMI, Reykjavik, Iceland |
Link |
2024 |
Conference |
S. Nabi, Ph. Esling, G. Peeters, F. Bevilacqua |
Embodied exploration of deep latent spaces in interactive dance-music performance |
In 9th International Conference on Movement and Computing, Utrecht, the Netherlands |
Link |
2024 |
Conference |
A. Riou, St. Lattner, G. Hadjeres, G. Peeters |
Investigating Design Choices in JEPA for General Audio Representation Learning |
In IEEE ICASSP, SASB workshop, Seoul, Korea |
Link |
2024 |
Conference |
B. Torres, G. Peeters, G. Richard |
Unsupervised harmonic parameter estimation using differentiable dsp and spectral optimal transport |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Seoul, Korea |
Link |
2024 |
Conference |
C. Peladeau, G. Peeters |
Blind estimation of audio effects using an auto-encoder approach and differentiable digital signal processing |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Seoul, Korea |
Link |
2024 |
Conference |
A. Quelennec, M. Olvera, G. Peeters, S. Essid |
On the choice of the optimal temporal support for audio classification with pre-trained embeddings |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Seoul, Korea |
Link |
2024 |
Conference |
A. Gagneré, S. Essid, G. Peeters |
Adapting pitch-based self supervised learning models for tempo estimation |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Seoul, Korea |
Link |
- |
- |
- |
- |
- |
- |
2023 |
Conference |
G. Peeters |
Self-Similarity-Based and Novelty-based loss for music structure analysis |
In Proc. of ISMIR (International Society for Music Information Retrieval), Milano, Italy |
Link |
2023 |
Conference |
A. Riou, St. Lattner, G. Hadjeres, G. Peeters |
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective |
In Proc. of ISMIR (International Society for Music Information Retrieval), Milano, Italy |
Link |
2023 |
Conference |
F. Mathieu, T. Courtat, G. Richard, and G. Peeters |
Learning interpretable filters in wav-unet for speech enhancement |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Rhodes, Greece |
Link |
2023 |
Conference |
F. Angulo, S. Essid, G. Peeters, and C. Mietlicki |
Cosmopolite sound monitoring (cosmo) : A study of urban sound event detection systems generalizing to multiple cities |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Rhodes, Greece |
Link |
- |
- |
- |
- |
- |
- |
2022 |
Conference/ LBD |
G. Peeters and F. Angulo |
Ssm-net: Feature learning for music structure analysis using differentiable self-similarity-matrix |
In Proc. of ISMIR (International Society for Music Information Retrieval), Bengaluru, India |
Link |
2022 |
Conference |
K. M. Ibrahim, E. V. Epure, G. Peeters, and G. Richard |
Exploiting device and audio data to tag music with user-aware listening contexts |
In Proc. of ISMIR (International Society for Music Information Retrieval), Bengaluru, India |
Link |
2022 |
Conference |
F. Mathieu, T. Courtat, G. Richard, and G. Peeters |
Phase shifted bedrosian filterbank: An interpretable audio front-end for time-domain audio source separatio |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Singapore, Singapore |
Link |
2022 |
Journal |
P. Proutskova, D. Wolff, G. Fazekas, K. Frieler, F. Ho ̈ger, O. Velichkina, G. Solis, T. Weyde, M. Pfleiderer, H.-C. Crayencour, G. Peeters, and S. Dixon |
The jazz ontology: A semantic model and large-scale rdf repositories for jazz |
Journal of Web Semantics |
Link |
2022 |
Journal |
C. Weiß and G. Peeters |
Comparing deep models and evaluation strategies for multi-pitch estimation in music recordings |
IEEE Transactions on Audio, Speech and Language Processing |
Link |
2022 |
Journal |
L. Pretet, G. Richard, C. Souchier, and G. Peeters |
Video-to-music recommendation using temporal alignment of segments |
IEEE Transactions on Multimedia |
Link |
2022 |
Journal |
H. Foroughmand and G. Peeters. |
Extending Deep Rhythm for Tempo and Genre Estimation Using Complex Convolutions, Multitask Learning and Multi-input Network |
Journal of Creative Music System |
Link |
- |
- |
- |
- |
- |
- |
2021 |
Journal |
M. Fell, Y. Nechaev, G. Meseguer-Brocal, E. Cabrio, F. Gando, and G. Peeters |
Lyrics segmentation via bimodal text-audio representation |
Natural Language Engineering |
Link |
2021 |
Conference |
C. Weiß and G. Peeters |
Learning multi-pitch estimation from weakly aligned score–audio pairs using a multi-label CTC loss |
In Proc. of IEEE WASPAA (Workshop on Applications of Signal Processing to Audio and Acoustics), New Paltz, NY, USA |
Link |
2021 |
Conference |
L. Pretet, G. Richard, and G. Peeters |
Music-video recommendation cross-modal music-video recommen- dation: A study of design choice |
In Proc. of IJCNN (International Joint Conference on Neural Networks), Virtual Event (Shenzhen, China) |
Link |
2021 |
Conference |
C. Weiß and G. Peeters |
Training deep pitch-class representations with a multi-label CTC loss |
In Proc. of ISMIR (International Society for Music Information Retrieval), Online |
Link |
2021 |
Conference |
L. Pretet, G. Richard, and G. Peeters |
Is there a “language of music-video clips” ? a qualitative and quantitative study |
In Proc. of ISMIR (International Society for Music Information Retrieval), Online |
Link |
2021 |
Book chapter |
G. Peeters |
The deep learning revolution in mir: the pros and cons, the needs and the challenges |
In LNCS 12631 - Perception, Representations, Image, Sound, Music. CMMR 2019, Lecture Notes in Computer Science. Springer-Verlag |
Link |
2021 |
Book chapter |
G. Peeters and G. Richard |
Deep learning for audio and music |
In J. Benois-Pineau and Z. Akka, editors, Multi-faceted Deep Learning: Models and Data, chapter 11. Springer Verlag |
Link |
- |
- |
- |
- |
- |
- |
2020 |
Conference |
G. Meseguer Brocal and G. Peeters |
Content based singing voice source separation via strong conditioning using aligned phonemes |
In Proc. of ISMIR (International Society for Music Information Retrieval), Montreal, Canada |
Link |
2020 |
Conference |
K. M. Ibrahim, E. V. Epure, G. Peeters, and G. Richard |
*Should we consider the users in contextual music auto-tagging models? * |
In Proc. of ISMIR (International Society for Music Information Retrieval), Montreal, Canada |
Link |
2020 |
Conference |
G. Doras, F. Yesiler, J. Serra, E. Gomez, and G. Peeters |
Combining musical features for cover detection |
In Proc. of ISMIR (International Society for Music Information Retrieval), Montreal, Canada |
Link |
2020 |
Conference |
H. Foroughmand and G. Peeters. |
Extending deep rhythm for tempo and genre estimation using complex convolutions, multitask learning and multi-input network |
In Proc. of Joint Conference on AI Music Creativity, Royal Institute of Technology (KTH), Stockholm, Sweden |
Link |
2020 |
Conference |
K. M. Ibrahim, E. V. Epure, G. Peeters, and G. Richard |
Confidence-based weighted loss for multi- label classification with missing labels |
In ACM International Conference on Multimedia Retrieval 2020 (ICMR 2020), Dublin, Ireland |
Link |
2020 |
Conference |
L. Pretet, G. Richard, and G. Peeters |
Learning to rank music tracks using triplet loss |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Barcelona, Spain |
Link |
2020 |
Conference |
G. Doras and G. Peeters |
A prototypical triplet loss for cover detection |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Barcelona, Spain |
Link |
2020 |
Conference |
K. M. Ibrahim, J. Royo-Letelier, E. Epure, G. Peeters, and G. Richard |
Audio-based auto-tagging with contextual tags for music |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Barcelona, Spain |
Link |
- |
- |
- |
- |
- |
- |
2019 |
Journal |
R. Mignot and G. Peeters |
An analysis of the effect of data augmentation methods: Experiments for a musical genre classification task |
Transactions of the International Society for Music Information Retrieval, 2(1):97–110 |
Link |
2019 |
Conference |
K. Frieler, D. Basaran, F. Hoger, H.-C. Crayencour, G. Peeters, and S. Dixon. |
Don’t hide in the frames: Note- and pattern-based evaluation of automated melody extraction algorithms |
In Proc. of DLfM (International Conference on Digital Libraries for Musicology), The Hague, The Netherlands |
Link |
2019 |
Conference |
G. Doras and G. Peeters |
Cover detection using dominant melody embeddings |
In Proc. of ISMIR (International Society for Music Information Retrieval), Delft, The Netherlands |
Link |
2019 |
Conference |
H. Foroughmand and G. Peeters |
Deep-rhythm for global tempo estimation in music |
In Proc. of ISMIR (International Society for Music Information Retrieval), Delft, The Netherlands |
Link |
2019 |
Conference |
G. Meseguer Brocal and G. Peeters |
Conditioned-u-net: Introducing a control mechanism in the u-net for multiple source separations |
In Proc. of ISMIR (International Society for Music Information Retrieval), Delft, The Netherlands |
Link |
2019 |
Conference |
A. Cohen-Hadria, A. Roebel, and G. Peeters |
Improving singing voice separation using deep u-net and wave-u-net with data augmentation |
In Proc. of EUSIPCO (European Signal Processing Conference), Coruña, Spain |
Link |
2019 |
Conference |
G. Doras, P. Esling, and G. Peeters |
On the use of u-net for dominant melody estimation in polyphonic music |
In Proc. of First International Workshop on Multilayer Music Representation and Processing (MMRP19), Milan, Italy |
Link |
- |
- |
- |
- |
- |
- |
2018 |
Conference |
D. Basaran, S. Essid, and G. Peeters |
Main melody extraction with source-filter nmf and c-rnn. |
In Proc. of ISMIR (International Society for Music Information Retrieval), Paris, France |
Link |
2018 |
Conference |
G. Meseguer Brocal, A. Cohen-Hadria, and G. Peeters |
Dali: A large dataset of synchronized audio, lyrics and pitch, automatically created using teacher-student |
In Proc. of ISMIR (International Society for Music Information Retrieval), Paris, France |
Link |
2018 |
Conference |
H. Foroughmand and G. Peeters |
Music retiler: Using nmf2d source separation for audio mosaicing |
In Audio Mostly (a conference on intercation with sound), Wrexham Glyndwr University (North Wales, UK) |
Link |
2018 |
Journal |
D. Fourer, F. Auger, and G. Peeters. |
Local am/fm parameters estimation: application to sinusoidal modeling and blind audio source separation |
IEEE Signal Processing Letters, 25(10):1600 – 1604 |
Link |
2018 |
Conference |
D. Fourer and G. Peeters |
Fast and adaptive blind audio source separation using recursive levenberg- marquardt synchrosqueezing |
In Proc. of IEEE ICASSP (International Conference on Acoustics, Speech, and Signal Processing), Calgary, Alberta, Canada |
Link |