--- title: "Signal Processing — papers" description: "Signal Processing: 153 papers of the Elamur Research library · page 1 of 3 · 1–25, the most cited first — our summary of each, its ideas, references and…" canonical: "https://research.elamur.ai/free/en/shelves/computer-science/signal-processing/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/computer-science/signal-processing/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) / Signal Processing # 🔊 Signal Processing 153 papers · page 1 of 3 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Signal Processing is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Speech and Audio Processing», «Music and Audio Processing», «Data Management and Algorithms». ## Papers **1** [2](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/page/3/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/page/2/) 1. [**WaveNet: A Generative Model for Raw Audio**Aaron van den Oord, Sander Dieleman, Heiga Zen, et al.*2016 · 5,167 citations*](https://md.research.elamur.ai/free/en/papers/oord-2016-wavenet-generative-model-raw-audio/) 2. [**SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition**Daniel S. Park, William Chan, Yu Zhang, et al.*2019 · 3,802 citations*](https://md.research.elamur.ai/free/en/papers/park-2019-specaugment-simple-data-augmentation-method/) 3. [**HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units**Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.*2021 · 3,441 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2021-hubert-self-supervised-speech-representation/) 4. [**Conformer: Convolution-augmented Transformer for Speech Recognition**Anmol Gulati, James Qin, Chung-Cheng Chiu, et al.*2020 · 3,433 citations*](https://md.research.elamur.ai/free/en/papers/gulati-2020-conformer-convolution-augmented-transformer-speech/) 5. [**Robust Speech Recognition via Large-Scale Weak Supervision**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,497 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/) 6. [**Deep Speech 2: End-to-End Speech Recognition in English and Mandarin**Dario Amodei, Rishita Anubhai, Eric Battenberg, et al.*2015 · 2,302 citations*](https://md.research.elamur.ai/free/en/papers/amodei-2015-deep-speech-2-end-end/) 7. [**Multimodal Transformer for Unaligned Multimodal Language Sequences**Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, et al.*2019 · 2,170 citations*](https://md.research.elamur.ai/free/en/papers/tsai-2019-multimodal-transformer-unaligned-multimodal-language/) 8. [**Attention-Based Models for Speech Recognition**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,776 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/) 9. [**wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations**Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, et al.*2020 · 1,355 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2020-wav2vec-2-0-framework-self/) 10. [**XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale**Arun Babu, Changhan Wang, Andros Tjandra, et al.*2021 · 799 citations*](https://md.research.elamur.ai/free/en/papers/babu-2021-xls-r-self-supervised-cross/) 11. [**Common Voice: A Massively-Multilingual Speech Corpus**Rosana Ardila, Megan Branson, Kelly Davis, et al.*2019 · 733 citations*](https://md.research.elamur.ai/free/en/papers/ardila-2019-common-voice-massively-multilingual-speech/) 12. [**SoundStream: An End-to-End Neural Audio Codec**Neil Zeghidour, Alejandro Luebs, Ahmed Omran, et al.*2021 · 727 citations*](https://md.research.elamur.ai/free/en/papers/zeghidour-2021-soundstream-end-end-neural-audio/) 13. [**Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces**Alice Coucke, Alaa Saade, Adrien Ball, et al.*2018 · 593 citations*](https://md.research.elamur.ai/free/en/papers/coucke-2018-snips-voice-platform-embedded-spoken/) 14. [**AudioLM: a Language Modeling Approach to Audio Generation**Zalan Borsos, Raphael Marinier, Damien Vincent, et al.*2022 · 570 citations*](https://md.research.elamur.ai/free/en/papers/borsos-2022-audiolm-language-modeling-approach-audio/) 15. [**The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text**Christopher Cieri, David Miller, Kevin Walker*2004 · 558 citations*](https://md.research.elamur.ai/free/en/papers/cieri-2004-fisher-corpus-resource-next-generations/) 16. [**MLS: A Large-Scale Multilingual Dataset for Speech Research**Vineel Pratap, Qiantong Xu, Anuroop Sriram, et al.*2020 · 510 citations*](https://md.research.elamur.ai/free/en/papers/pratap-2020-mls-large-scale-multilingual-dataset/) 17. [**Discourse Segmentation of Multi-Party Conversation**Michel Galley, Kathleen R. McKeown, Eric Fosler-Lussier, et al.*2003 · 455 citations*](https://md.research.elamur.ai/free/en/papers/galley-2003-discourse-segmentation-multi-party-conversation/) 18. [**High Fidelity Neural Audio Compression**Alexandre Defossez, Jade Copet, Gabriel Synnaeve, et al.*2022 · 412 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2022-high-fidelity-neural-audio-compression/) 19. [**FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech**Alexis Conneau, Min Ma, Simran Khanuja, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2022-fleurs-few-shot-learning-evaluation/) 20. [**Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs**Yu. A. Malkov, D. A. Yashunin*2016 · 341 citations*](https://md.research.elamur.ai/free/en/papers/malkov-2016-efficient-robust-approximate-nearest-neighbor/) 21. [**Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers**Chengyi Wang, Sanyuan Chen, Yu Wu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-neural-codec-language-models-zero/) 22. [**Scaling Speech Technology to 1,000+ Languages**Vineel Pratap, Andros Tjandra, Bowen Shi, et al.*2023 · 331 citations*](https://md.research.elamur.ai/free/en/papers/pratap-2023-scaling-speech-technology-1-000/) 23. [**Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq**Changhan Wang, Yun Tang, Xutai Ma, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-fairseq-s2t-fast-speech-text/) 24. [**SALMONN: Towards Generic Hearing Abilities for Large Language Models**Changli Tang, Wenyi Yu, Guangzhi Sun, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-salmonn-towards-generic-hearing-abilities/) 25. [**SeamlessM4T: Massively Multilingual & Multimodal Machine Translation**Seamless Communication, Loic Barrault, Yu-An Chung, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/communication-2023-seamlessm4t-massively-multilingual-multimodal-machine/) **1** [2](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/page/3/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/page/2/) Updated 23 October 2026 - [← previous subfield Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) - [next subfield → Software](https://md.research.elamur.ai/free/en/shelves/computer-science/software/) - [↑ back to Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/)