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Relevant bibliographies by topics / Bottleneck auto-encoder

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Academic literature on the topic 'Bottleneck auto-encoder'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Bottleneck auto-encoder"

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Bous, Frederik, and Axel Roebel. "A Bottleneck Auto-Encoder for F0 Transformations on Speech and Singing Voice." Information 13, no. 3 (2022): 102. http://dx.doi.org/10.3390/info13030102.

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In this publication, we present a deep learning-based method to transform the f0 in speech and singing voice recordings. f0 transformation is performed by training an auto-encoder on the voice signal’s mel-spectrogram and conditioning the auto-encoder on the f0. Inspired by AutoVC/F0, we apply an information bottleneck to it to disentangle the f0 from its latent code. The resulting model successfully applies the desired f0 to the input mel-spectrograms and adapts the speaker identity when necessary, e.g., if the requested f0 falls out of the range of the source speaker/singer. Using the mean f

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Ullmann, Denis, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Brandon Panos, and Slava Voloshynovskiy. "Information Bottleneck Classification in Extremely Distributed Systems." Entropy 22, no. 11 (2020): 1237. http://dx.doi.org/10.3390/e22111237.

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We present a new decentralized classification system based on a distributed architecture. This system consists of distributed nodes, each possessing their own datasets and computing modules, along with a centralized server, which provides probes to classification and aggregates the responses of nodes for a final decision. Each node, with access to its own training dataset of a given class, is trained based on an auto-encoder system consisting of a fixed data-independent encoder, a pre-trained quantizer and a class-dependent decoder. Hence, these auto-encoders are highly dependent on the class

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Nguyen, Bao Quoc, Thang Tat Vu, and 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, no. 4 (2016): 267. http://dx.doi.org/10.15625/1813-9663/31/4/5944.

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In this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel (VOV). The results show that the DBNF extraction for Vietnamese recognition decreases relative word error rate by 14 % and 39 % compared to the base bottleneck features and MFCC baseline, respectively.

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Wang, Mou, Xiao-Lei Zhang, and Susanto Rahardja. "An Unsupervised Deep Learning System for Acoustic Scene Analysis." Applied Sciences 10, no. 6 (2020): 2076. http://dx.doi.org/10.3390/app10062076.

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Acoustic scene analysis has attracted a lot of attention recently. Existing methods are mostly supervised, which requires well-predefined acoustic scene categories and accurate labels. In practice, there exists a large amount of unlabeled audio data, but labeling large-scale data is not only costly but also time-consuming. Unsupervised acoustic scene analysis on the other hand does not require manual labeling but is known to have significantly lower performance and therefore has not been well explored. In this paper, a new unsupervised method based on deep auto-encoder networks and spectral cl

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Nguyen, VietHung, and 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, no. 1 (2022): 8653–61. http://dx.doi.org/10.15282/jmes.16.1.2022.01.0684.

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In the trend of Industry 4.0 development, the big data of system operation is significant for analyzing, predicting, or identifying any possible problem. This study proposes a new diagnosis technique for identifying the vibration signal, which combines the feature dimensional reduction method and optimized classifier. Firstly, an auto-encoder feature dimensional reduction (AE-FDR) method is constructed with the bottleneck hidden layer to extract the low-dimensional feature. Secondly, a supervised classifier is formed to carry out fine-turning and classification. The least square-support vector

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Mahesh T R, V Vivek, and Vinoth Kumar. "Implementation of Machine Learning-Based Data Mining Techniques for IDS." International Journal of Information Technology, Research and Applications 2, no. 1 (2023): 7–13. http://dx.doi.org/10.59461/ijitra.v2i1.23.

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The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract user

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Mahesh, T. R., Vivek V, and Kumar Vinoth. "Implementation of Machine Learning-Based Data Mining Techniques for IDS." International Journal of Information Technology, Research and Applications (IJITRA) ISSN: 2583 5343 2, no. 1 (2023): 7–13. https://doi.org/10.5281/zenodo.7780113.

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The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract

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8

Wang, Lei, Xuanrui Ren, and Chunyi Wu. "An Intrusion Detection Method Based on Symmetric Federated Deep Learning in Complex Networks." Symmetry 17, no. 6 (2025): 952. https://doi.org/10.3390/sym17060952.

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The rapid development of the current 5G/6G network has added tremendous pressure to traditional security detection in the scenario of dealing with large-scale network attacks, resulting in high time complexity and low efficiency of attack identification. According to the deep network and its symmetry principle, this paper proposes a complex network intrusion detection and recognition method based on symmetric federation optimization, named IDS, which aims to reduce the time complexity and improve the accuracy and efficiency of attack identification. By using a symmetric network UNet-based deep

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Kadam, Sanjay Shahaji, Jotiram Krishna Deshmukh, Ankush Madhukar Gund, Sudam Vasant Nikam, Vijay Balaso Mane, and 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, no. 8 (2023): 145–59. http://dx.doi.org/10.17762/ijritcc.v11i8.7932.

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The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced

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Wang, Zichen. "Music Composition Based on Generative Modeling with Artificial Intelligence: Challenges, Innovations and Perspectives." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 1–7. https://doi.org/10.54097/e682hj48.

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In today's era of rapid technological advancement, the emergence of artificial intelligence has brought new opportunities to the field of music creation. Music, as an important part of human culture, has expressed emotions in its own unique way since ancient times and has evolved and innovated over time. This paper introduces the progress made by artificial intelligence in music creation, describes the application and innovation of multi-granular attention Transformer, Variable Auto-Encoder (VAE), and Generative Adversarial Network (GAN) and other technological models in the field of music gen

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Dissertations / Theses on the topic "Bottleneck auto-encoder"

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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.

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La voix humaine est une grande source de fascination et un objet de recherche depuis plus de 100 ans. Pendant ce temps, de nombreuses technologies ont germées autour de la voix, comme le vocodeur, qui fournit une représentation paramétrique de la voix, couramment utilisée pour la transformation de la voix. Dans cette tradition, les limites des approches basées uniquement sur le traitement du signal sont évidentes : Pour créer des transformations cohérentes, les dépendances entre les différentes propriétés vocales doivent être bien comprises et modélisées avec précision. Modéliser ces corrélati

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Conference papers on the topic "Bottleneck auto-encoder"

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Sainath, Tara N., Brian Kingsbury, and 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.

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Koike-Akino, Toshiaki, and 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.

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Abolhasanzadeh, 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.

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Jamil, Ahmad, Tina Dinh, Ward De Paepe, and Homam Nikpey Somehsaraei. "The Use of Artificial Neural Networks With a Systems Engineering Approach for Data Enhancement in Humidified Micro-Gas Turbine Application." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-124906.

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Abstract Globally the energy sector is being transformed by innovations based on decarbonization, decentralization, and digitalization. These encompass transitioning towards a future with distributed generation systems having an increased share of renewables, artificial intelligence, and digital solutions. Micro-gas turbines are a competitive enabling technology in distributed power generation owing to benefits like fuel flexibility, reduced operation and maintenance costs, and emissions. However, its economic performance stumbles when not running in combined heat and power mode, requiring cyc

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