Auswahl der wissenschaftlichen Literatur zum Thema „Bottleneck auto-encoder“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Inhaltsverzeichnis
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Bottleneck auto-encoder" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Bottleneck auto-encoder"
Bous, Frederik, und Axel Roebel. „A Bottleneck Auto-Encoder for F0 Transformations on Speech and Singing Voice“. Information 13, Nr. 3 (23.02.2022): 102. http://dx.doi.org/10.3390/info13030102.
Der volle Inhalt der QuelleUllmann, Denis, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Brandon Panos und Slava Voloshynovskiy. „Information Bottleneck Classification in Extremely Distributed Systems“. Entropy 22, Nr. 11 (30.10.2020): 1237. http://dx.doi.org/10.3390/e22111237.
Der volle Inhalt der QuelleNguyen, Bao Quoc, Thang Tat Vu und Mai Chi Luong. „Improving bottleneck features for Vietnamese large vocabulary continuous speech recognition system using deep neural networks“. Journal of Computer Science and Cybernetics 31, Nr. 4 (03.01.2016): 267. http://dx.doi.org/10.15625/1813-9663/31/4/5944.
Der volle Inhalt der QuelleWang, Mou, Xiao-Lei Zhang und Susanto Rahardja. „An Unsupervised Deep Learning System for Acoustic Scene Analysis“. Applied Sciences 10, Nr. 6 (19.03.2020): 2076. http://dx.doi.org/10.3390/app10062076.
Der volle Inhalt der QuelleNguyen, VietHung, und V.T. Pham. „Gear fault monitoring based on unsupervised feature dimensional reduction and optimized LSSVM-BSOA machine learning model“. Journal of Mechanical Engineering and Sciences 16, Nr. 1 (23.03.2022): 8653–61. http://dx.doi.org/10.15282/jmes.16.1.2022.01.0684.
Der volle Inhalt der QuelleMahesh T R, V Vivek und Vinoth Kumar. „Implementation of Machine Learning-Based Data Mining Techniques for IDS“. International Journal of Information Technology, Research and Applications 2, Nr. 1 (31.03.2023): 7–13. http://dx.doi.org/10.59461/ijitra.v2i1.23.
Der volle Inhalt der QuelleKadam, Sanjay Shahaji, Jotiram Krishna Deshmukh, Ankush Madhukar Gund, Sudam Vasant Nikam, Vijay Balaso Mane und Dayanand Raghoba Ingle. „Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm“. International Journal on Recent and Innovation Trends in Computing and Communication 11, Nr. 8 (20.09.2023): 145–59. http://dx.doi.org/10.17762/ijritcc.v11i8.7932.
Der volle Inhalt der QuelleÇETİN, Yarkın Deniz, und Ramazan Gökberk CİNBİŞ. „Attentive Sequential Auto-Encoding Towards Unsupervised Object-centric Scene Modeling“. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 15.11.2022. http://dx.doi.org/10.29109/gujsc.1139701.
Der volle Inhalt der QuelleSong, Junjie, Rong Huang, Yujia Tian und Aihua Dong. „Pre-Activating Semantic Information for Image Aesthetic Assessment“. AATCC Journal of Research, 05.02.2023, 247234442211479. http://dx.doi.org/10.1177/24723444221147971.
Der volle Inhalt der QuelleDissertationen zum Thema "Bottleneck auto-encoder"
Bous, Frederik. „A neural voice transformation framework for modification of pitch and intensity“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS382.
Der volle Inhalt der QuelleHuman voice has been a great source of fascination and an object of research for over 100 years. During that time numerous technologies have sprouted around the voice, such as the vocoder, which provides a parametric representation of the voice, commonly used for voice transformation. From this tradition, the limitations of purely signal processing based approaches are evident: To create meaningful transformations the codependencies between different voice properties have to be understood well and modelled precisely. Modelling these correlations with heuristics obtained by empiric studies is not sufficient to create natural results. It is necessary to extract information about the voice systematically and use this information during the transformation process automatically. Recent advances in computer hardware permit this systematic analysis of data by means of machine learning. This thesis thus uses machine learning to create a neural voice transformation framework. The proposed neural voice transformation framework works in two stages: First a neural vocoder allows mapping between a raw audio and a mel-spectrogram representation of voice signals. Secondly, an auto-encoder with information bottleneck allows disentangling various voice properties from the remaining information. The auto-encoder allows changing one voice property while automatically adjusting the remaining voice properties. In the first part of this thesis, we discuss different approaches to neural vocoding and reason why the mel-spectrogram is better suited for neural voice transformations than conventional parametric vocoder spaces. In the second part we discuss the information bottleneck auto-encoder. The auto-encoder creates a latent code that is independent of its conditional input. Using the latent code the synthesizer can perform the transformation by combining the original latent code with a modified parameter curve. We transform the voice using two control parameters: the fundamental frequency and the voice level. Transformation of the fundamental frequency is an objective with a long history. Using the fundamental frequency allows us to compare our approach to existing techniques and study how the auto-encoder models the dependency on other properties in a well known environment. For the voice level, we face the problem that annotations hardly exist. Therefore, first we provide a new estimation technique for voice level in large voice databases, and subsequently use the voice level annotations to train a bottleneck auto-encoder that allows changing the voice level
Konferenzberichte zum Thema "Bottleneck auto-encoder"
Sainath, Tara N., Brian Kingsbury und Bhuvana Ramabhadran. „Auto-encoder bottleneck features using deep belief networks“. In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288833.
Der volle Inhalt der QuelleKoike-Akino, Toshiaki, und Ye Wang. „Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction“. In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174523.
Der volle Inhalt der QuelleAbolhasanzadeh, Bahareh. „Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features“. In 2015 7th Conference on Information and Knowledge Technology (IKT). IEEE, 2015. http://dx.doi.org/10.1109/ikt.2015.7288799.
Der volle Inhalt der Quelle