Auswahl der wissenschaftlichen Literatur zum Thema „Auditory attention decoding“
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Zeitschriftenartikel zum Thema "Auditory attention decoding"
Han, Cong, James O’Sullivan, Yi Luo, Jose Herrero, Ashesh D. Mehta und Nima Mesgarani. „Speaker-independent auditory attention decoding without access to clean speech sources“. Science Advances 5, Nr. 5 (Mai 2019): eaav6134. http://dx.doi.org/10.1126/sciadv.aav6134.
Der volle Inhalt der QuelleAldag, Nina, Andreas Büchner, Thomas Lenarz und Waldo Nogueira. „Towards decoding selective attention through cochlear implant electrodes as sensors in subjects with contralateral acoustic hearing“. Journal of Neural Engineering 19, Nr. 1 (01.02.2022): 016023. http://dx.doi.org/10.1088/1741-2552/ac4de6.
Der volle Inhalt der QuelleGeirnaert, Simon, Servaas Vandecappelle, Emina Alickovic, Alain de Cheveigne, Edmund Lalor, Bernd T. Meyer, Sina Miran, Tom Francart und Alexander Bertrand. „Electroencephalography-Based Auditory Attention Decoding: Toward Neurosteered Hearing Devices“. IEEE Signal Processing Magazine 38, Nr. 4 (Juli 2021): 89–102. http://dx.doi.org/10.1109/msp.2021.3075932.
Der volle Inhalt der QuelleFu, Zhen, Xihong Wu und Jing Chen. „Congruent audiovisual speech enhances auditory attention decoding with EEG“. Journal of Neural Engineering 16, Nr. 6 (06.11.2019): 066033. http://dx.doi.org/10.1088/1741-2552/ab4340.
Der volle Inhalt der QuelleStraetmans, L., B. Holtze, S. Debener, M. Jaeger und B. Mirkovic. „Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG“. Journal of Neural Engineering 18, Nr. 6 (01.12.2021): 066054. http://dx.doi.org/10.1088/1741-2552/ac42b5.
Der volle Inhalt der QuelleFacoetti, Andrea, Anna Noemi Trussardi, Milena Ruffino, Maria Luisa Lorusso, Carmen Cattaneo, Raffaella Galli, Massimo Molteni und Marco Zorzi. „Multisensory Spatial Attention Deficits Are Predictive of Phonological Decoding Skills in Developmental Dyslexia“. Journal of Cognitive Neuroscience 22, Nr. 5 (Mai 2010): 1011–25. http://dx.doi.org/10.1162/jocn.2009.21232.
Der volle Inhalt der QuelleXu, Zihao, Yanru Bai, Ran Zhao, Qi Zheng, Guangjian Ni und Dong Ming. „Auditory attention decoding from EEG-based Mandarin speech envelope reconstruction“. Hearing Research 422 (September 2022): 108552. http://dx.doi.org/10.1016/j.heares.2022.108552.
Der volle Inhalt der QuelleAroudi, Ali, und Simon Doclo. „Cognitive-Driven Binaural Beamforming Using EEG-Based Auditory Attention Decoding“. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 862–75. http://dx.doi.org/10.1109/taslp.2020.2969779.
Der volle Inhalt der QuelleAroudi, Ali, Eghart Fischer, Maja Serman, Henning Puder und Simon Doclo. „Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding“. Algorithms 14, Nr. 10 (30.09.2021): 287. http://dx.doi.org/10.3390/a14100287.
Der volle Inhalt der QuelleWang, Lei, Ed X. Wu und Fei Chen. „EEG-based auditory attention decoding using speech-level-based segmented computational models“. Journal of Neural Engineering 18, Nr. 4 (25.05.2021): 046066. http://dx.doi.org/10.1088/1741-2552/abfeba.
Der volle Inhalt der QuelleDissertationen zum Thema "Auditory attention decoding"
Aroudi, Ali [Verfasser]. „Cognitive-Driven Speech Enhancement using EEG-based Auditory Attention Decoding for Hearing Aid Applications / Ali Aroudi“. München : Verlag Dr. Hut, 2021. http://d-nb.info/1232846716/34.
Der volle Inhalt der QuelleCantisani, Giorgia. „Neuro-steered music source separation“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT038.
Der volle Inhalt der QuelleIn this PhD thesis, we address the challenge of integrating Brain-Computer Interfaces (BCI) and music technologies on the specific application of music source separation, which is the task of isolating individual sound sources that are mixed in the audio recording of a musical piece. This problem has been investigated for decades, but never considering BCI as a possible way to guide and inform separation systems. Specifically, we explored how the neural activity characterized by electroencephalographic signals (EEG) reflects information about the attended instrument and how we can use it to inform a source separation system.First, we studied the problem of EEG-based auditory attention decoding of a target instrument in polyphonic music, showing that the EEG tracks musically relevant features which are highly correlated with the time-frequency representation of the attended source and only weakly correlated with the unattended one. Second, we leveraged this ``contrast'' to inform an unsupervised source separation model based on a novel non-negative matrix factorisation (NMF) variant, named contrastive-NMF (C-NMF) and automatically separate the attended source.Unsupervised NMF represents a powerful approach in such applications with no or limited amounts of training data as when neural recording is involved. Indeed, the available music-related EEG datasets are still costly and time-consuming to acquire, precluding the possibility of tackling the problem with fully supervised deep learning approaches. Thus, in the last part of the thesis, we explored alternative learning strategies to alleviate this problem. Specifically, we propose to adapt a state-of-the-art music source separation model to a specific mixture using the time activations of the sources derived from the user's neural activity. This paradigm can be referred to as one-shot adaptation, as it acts on the target song instance only.We conducted an extensive evaluation of both the proposed system on the MAD-EEG dataset which was specifically assembled for this study obtaining encouraging results, especially in difficult cases where non-informed models struggle
Buchteile zum Thema "Auditory attention decoding"
Nasrin, Fatema, Nafiz Ishtiaque Ahmed und Muhammad Arifur Rahman. „Auditory Attention State Decoding for the Quiet and Hypothetical Environment: A Comparison Between bLSTM and SVM“. In Advances in Intelligent Systems and Computing, 291–301. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4673-4_23.
Der volle Inhalt der QuelleGeirnaert, Simon, Rob Zink, Tom Francart und Alexander Bertrand. „Fast, Accurate, Unsupervised, and Time-Adaptive EEG-Based Auditory Attention Decoding for Neuro-steered Hearing Devices“. In SpringerBriefs in Electrical and Computer Engineering, 29–40. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49457-4_4.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Auditory attention decoding"
Wang, Liting, Xintao Hu, Meng Wang, Jinglei Lv, Junwei Han, Shijie Zhao, Qinglin Dong, Lei Guo und Tianming Liu. „Decoding dynamic auditory attention during naturalistic experience“. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950678.
Der volle Inhalt der QuellePallenberg, René, Ann-Katrin Griedelbach und Alfred Mertins. „LSTMs for EEG-based Auditory Attention Decoding“. In 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023. http://dx.doi.org/10.23919/eusipco58844.2023.10289779.
Der volle Inhalt der QuelleQiu, Zelin, Jianjun Gu, Dingding Yao und Junfeng Li. „Exploring Auditory Attention Decoding using Speaker Features“. In INTERSPEECH 2023. ISCA: ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-414.
Der volle Inhalt der QuelleAlickovic, Emina, Carlos Francisco Mendoza, Andrew Segar, Maria Sandsten und Martin A. Skoglund. „Decoding Auditory Attention From EEG Data Using Cepstral Analysis“. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023. http://dx.doi.org/10.1109/icasspw59220.2023.10193192.
Der volle Inhalt der QuelleAroudi, Ali, Daniel Marquardt und Simon Daclo. „EEG-Based Auditory Attention Decoding Using Steerable Binaural Superdirective Beamformer“. In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462278.
Der volle Inhalt der QuelleAroudi, Ali, Marc Delcroix, Tomohiro Nakatani, Keisuke Kinoshita, Shoko Araki und Simon Doclo. „Cognitive-Driven Convolutional Beamforming Using EEG-Based Auditory Attention Decoding“. In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231657.
Der volle Inhalt der QuelleChen, Xiaoyu, Changde Du, Qiongyi Zhou und Huiguang He. „Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning“. In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581783.3611869.
Der volle Inhalt der QuelleHeintz, Nicolas, Simon Geirnaert, Tom Francart und Alexander Bertrand. „Unbiased Unsupervised Stimulus Reconstruction for EEG-Based Auditory Attention Decoding“. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096608.
Der volle Inhalt der QuelleFu, Zhen, Bo Wang, Xihong Wu und Jing Chen. „Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network“. In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616195.
Der volle Inhalt der QuelleAn, Winko W., Alexander Pei, Abigail L. Noyce und Barbara Shinn-Cunningham. „Decoding auditory attention from EEG using a convolutional neural network“. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630484.
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