| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 411 | 2024 |
| LEAF: A learnable frontend for audio classification N Zeghidour, O Teboul, FDC Quitry, M Tagliasacchi arXiv preprint arXiv:2101.08596, 2021 | 168 | 2021 |
| Towards learning a universal non-semantic representation of speech J Shor, A Jansen, R Maor, O Lang, O Tuval, FC Quitry, M Tagliasacchi, ... arXiv preprint arXiv:2002.12764, 2020 | 161 | 2020 |
| Acoustic modelling with cd-ctc-smbr lstm rnns A Senior, H Sak, F de Chaumont Quitry, T Sainath, K Rao 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU …, 2015 | 159 | 2015 |
| Audiopalm: A large language model that can speak and listen PK Rubenstein, C Asawaroengchai, DD Nguyen, A Bapna, Z Borsos, ... arXiv preprint arXiv:2306.12925, 2023 | 131 | 2023 |
| Pre-training audio representations with self-supervision M Tagliasacchi, B Gfeller, F de Chaumont Quitry, D Roblek IEEE Signal Processing Letters 27, 600-604, 2020 | 58 | 2020 |
| Self-supervised audio representation learning for mobile devices M Tagliasacchi, B Gfeller, FC Quitry, D Roblek arXiv preprint arXiv:1905.11796, 2019 | 49 | 2019 |
| Disentangling speech from surroundings with neural embeddings A Omran, N Zeghidour, Z Borsos, F de Chaumont Quitry, M Slaney, ... ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 12* | 2023 |
| Learning audio representations via phase prediction FC Quitry, M Tagliasacchi, D Roblek arXiv preprint arXiv:1910.11910, 2019 | 9 | 2019 |
| High quality agreement-based semi-supervised training data for acoustic modeling F de Chaumont Quitry, A Oines, P Moreno, E Weinstein 2016 IEEE Spoken Language Technology Workshop (SLT), 592-596, 2016 | 8 | 2016 |
| Learning audio representations via phase prediction F de Chaumont Quitry, M Tagliasacchi, D Roblek arXiv e-prints, arXiv: 1910.11910, 2019 | 5 | 2019 |
| Generating audio waveforms using encoder and decoder neural networks Y Li, M Tagliasacchi, D Roblek, F de Chaumont Quitry, B Gfeller, ... US Patent App. 17/856,292, 2023 | 3 | 2023 |
| Multi-task adapter neural networks M Tagliasacchi, F de Chaumont Quitry, D Roblek US Patent App. 17/764,005, 2022 | 2 | 2022 |
| Self-supervised audio representation learning for mobile devices B Gfeller, D Roblek, F de Chaumont Quitry, M Tagliasacchi US Patent 11,501,787, 2022 | 1 | 2022 |
| CycleGAN-Based Unpaired Speech Dereverberation H Muckenhirn, A Safin, H Erdogan, FC Quitry, M Tagliasacchi, S Wisdom, ... arXiv preprint arXiv:2203.15652, 2022 | 1 | 2022 |
| Methods and systems for implementing on-device non-semantic representation fine-tuning for speech classification J Shor, R Maor, O Lang, O Tuval, M Tagliasacchi, I Shavitt, ... US Patent 11,996,116, 2024 | | 2024 |
| Learned audio frontend machine learning model for audio understanding N Zeghidour, O Teboul, F de Chaumont Quitry, M Tagliasacchi US Patent App. 18/029,843, 2023 | | 2023 |
| Self-Supervised Audio Representation Learning for Mobile Devices B Gfeller, D Roblek, F de Chaumont Quitry, M Tagliasacchi US Patent App. 17/986,477, 2023 | | 2023 |
| Multi-Task Adapters for On-Device Audio Inference M Tagliasacchi, F de Chaumont Quitry, D Roblek IEEE Signal Processing Letters 27, 630-634, 2020 | | 2020 |
| Learning audio representations with self-supervision M Tagliasacchi, B Gfeller, F de Chaumont Quitry, D Roblek | | |
