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The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment
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In: ISSN: 2076-3263 ; Geosciences ; https://hal.archives-ouvertes.fr/hal-03168500 ; Geosciences, MDPI, 2021, ⟨10.3390/geosciences11030133⟩ (2021)
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Tackling Morphological Analogies Using Deep Learning -- Extended Version
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In: https://hal.inria.fr/hal-03425776 ; 2021 (2021)
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Speaker Attentive Speech Emotion Recognition
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In: Proccedings of interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03554368 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.2866-2870, ⟨10.21437/interspeech.2021-573⟩ (2021)
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Brain-Inspired Audio-Visual Information Processing Using Spiking Neural Networks
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Wendt, Anne. - : Auckland University of Technology, 2021
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Abstract:
Artificial neural networks are one of the most popular and promising approaches to modern machine learning applications. They are based on a mathematical abstraction of the intricate processing mechanisms in the human brain, remaining sufficiently simple for efficient processing in conventional computers. Despite efforts to mimic the capabilities of the brain, however, they are limited in their contextual understanding of concepts and behaviours. With the aim to explore ways to overcome these limitations, this thesis endeavours to investigate alternatives that are closer to the original biological systems, with a focus on processing auditory and visual signals. Inspired by the functioning of human hearing and vision and by the brain’s capabilities to dynamically integrate newly perceived information with previous experiences and knowledge, this thesis presents the hypothesis that mimicking these processes more closely could lead to an enhanced analysis of such signals. The framework that was developed to investigate this hypothesis consisted of three separate but connected projects that looked into biologically inspired computational processing of auditory, visual, and combined audio-visual signals, respectively. One aim of designing the framework was to largely preserve the spectral, spatial, and temporal characteristics of the original signals through tonotopic and retinotopic mapping. For the auditory processing system, an encoding and mapping method was developed that could transform sound signals into electrical impulses (“spikes”) by simulating the human cochlea, which were then fed into a brain-shaped three-dimensional spiking neural network at the location of the auditory cortices. For the visual system, the method was developed analogously, simulating the human retina and feeding the resulting spikes into the location of the visual cortex. A key advantage of this approach was that it facilitated a straightforward brain-like combination of input signals for the analysis of audio-visual stimuli during the third project. The approach was tested on two existing benchmark datasets and on one newly created New Zealand Sign Language dataset to explore its capabilities. While the sound processing system achieved good classification results on the chosen speech recognition dataset (91%) compared to existing methods in the same domain, the video processing system, which was tested on a gesture recognition dataset, did not perform as well (51%). The classification results for the combined audio-visual processing model were between those for the individual models (76.7%), and unique spike patterns for the five classes could be observed. Even though the models created in this work did not exceed the statistical achievements of conventional machine learning methods, they demonstrated that systems inspired by biological and neural mechanisms are a promising pathway to investigate audio-visual data in computational systems. Increasing the biological plausibility of the models is expected to lead to better performance and could form a pathway to a more intuitive understanding of such data. To broaden the applicability of the model, it is suggested that future work include the addition of other sensory modalities or signals acquired through different brain recording and imaging methods and to perform further theoretical and statistical analysis of the relationship between model parameters and classification performance.
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Keyword:
Gesture recognition; Machine learning; New Zealand sign language; Signal mapping; Speech recognition; Spiking neural networks
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URL: http://hdl.handle.net/10292/14526
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SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
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In: SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval ; https://hal.sorbonne-universite.fr/hal-03290774 ; SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 2021, Virtual Event, Canada. pp.2288-2292, ⟨10.1145/3404835.3463098⟩ (2021)
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Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
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In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
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What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
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In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
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Communicating artificial neural networks develop efficient color-naming systems
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In: ISSN: 0027-8424 ; EISSN: 1091-6490 ; Proceedings of the National Academy of Sciences of the United States of America ; https://hal.inria.fr/hal-03329084 ; Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2021, 118 (12), ⟨10.1073/pnas.2016569118⟩ (2021)
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Towards the prediction of the vocal tract shape from the sequence of phonemes to be articulated
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In: iNTERSPEECH 2021 ; https://hal.inria.fr/hal-03360113 ; iNTERSPEECH 2021, Aug 2021, Brno, Czech Republic (2021)
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| 10 |
Human cortical encoding of pitch in tonal and non-tonal languages.
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In: Nature communications, vol 12, iss 1 (2021)
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| 11 |
Graph Algorithms for Multiparallel Word Alignment
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; The 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03424044 ; The 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Nov 2021, Punta Cana, Dominica ; https://2021.emnlp.org/ (2021)
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| 12 |
Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios
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In: https://hal.inria.fr/hal-03327306 ; 2021 (2021)
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Multimodal Coarticulation Modeling : Towards the animation of an intelligible talking head ; Modélisation de la coarticulation multimodale : vers l'animation d'une tête parlante intelligible
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In: https://hal.univ-lorraine.fr/tel-03203815 ; Intelligence artificielle [cs.AI]. Université de Lorraine, 2021. Français. ⟨NNT : 2021LORR0019⟩ (2021)
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| 14 |
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
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In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203318 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia (2021)
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| 15 |
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
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In: https://hal.inria.fr/hal-03203374 ; 2021 (2021)
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| 16 |
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
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In: https://hal.inria.fr/hal-03203318 ; 2021 (2021)
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| 17 |
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
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In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203374 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia. pp.71--82, ⟨10.1007/978-3-030-86383-8_6⟩ ; https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6 (2021)
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| 18 |
Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
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In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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| 19 |
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
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In: ISBN: 978-1-6654-0019-0 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) ; https://hal.archives-ouvertes.fr/hal-03405970 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Sep 2021, Nara, Japan ; https://www.acii-conf.net/2021/ (2021)
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| 20 |
Exploitation du corpus Democrat par apprentissage artificiel
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In: ISSN: 0458-726X ; EISSN: 1958-9549 ; Langages ; https://hal.archives-ouvertes.fr/hal-03475070 ; Langages, Armand Colin (Larousse jusqu'en 2003), 2021, Un corpus annoté en chaînes de référence et son exploitation– le projet Democrat, 224 (2021)
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