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检索条件"主题词=denoising autoencoder"

共 340 条记录，以下是261-270 订阅

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Tactile Object Recognition using Deep Learning and Dropout 14

Tactile Object Recognition using Deep Learning and Dropout

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14th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Schmitz, Alexander Bansho, Yusuke Noda, Kuniaki Iwata, Hiroyasu Ogata, Tetsuya Sugano, Shigeki Waseda Univ Sch Creat Sci & Engn Sugano Lab Shinjuku Ku Okubo 2-4-12 Tokyo 1690072 Japan Waseda Univ Sch Creat Sci & Engn Sugano Lab Shinjuku Ku Tokyo 1620044 Japan Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo 1698555 Japan Waseda Univ Sch Creat Sci & Engn Dept Modern Mech Engn Tokyo Japan Waseda Univ Sch Creat Sci & Engn Dept Modern Mech Engn Shinjuku Ku Tokyo 1698555 Japan

ISBN: (纸本)9781479971749

Recognizing grasped objects with tactile sensors is beneficial in many situations, as other sensor information like vision is not always reliable. In this paper, we aim for multimodal object recognition by power grasping of objects with an unknown orientation and position relation to the hand. Few robots have the necessary tactile sensors to reliably recognize objects: in this study the multifingered hand of TWENDY-ONE is used, which has distributed skin sensors covering most of the hand, 6 axis F/T sensors in each fingertip, and provides information about the joint angles. Moreover, the hand is compliant. When using tactile sensors, it is not clear what kinds of features are useful for object recognition. Recently, deep learning has shown promising results. Nevertheless, deep learning has rarely been used in robotics and to our best knowledge never for tactile sensing, probably because it is difficult to gather many samples with tactile sensors. Our results show a clear improvement when using a denoising autoencoder with dropout compared to traditional neural networks. Nevertheless, a higher number of layers did not prove to be beneficial.

关键词： control engineering computing dexterous manipulators distributed sensors learning (artificial intelligence) object recognition tactile sensors F/T sensors TWENDY-ONE deep learning denoising autoencoder distributed skin sensors dropout grasped objects recognition joint angles multifingered hand multimodal object recognition position relation power grasping tactile object recognition unknown orientation Object recognition Principal component analysis Skin Tactile sensors Training tactile sensors object recognition dexterous manipulators control engineering computing distributed sensors Machine Learning principal components analysis skin

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Compensate multiple distortions for speaker recognition systems 29

Compensate multiple distortions for speaker recognition syst...

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29th European Signal Processing Conference (EUSIPCO)

作者： Mohammadamini, Mohammad Matrouf, Driss Bonastre, Jean-Francois Serizel, Romain Dowerah, Sandipana Jouvet, Denis Avignon Univ LTA Lab Informat Avignon Avignon France Univ Lorraine CNRS INRIA Loria F-54000 Nancy France

ISBN: (纸本)9789082797060

The performance of speaker recognition systems reduces dramatically in severe conditions in the presence of additive noise and/or reverberation. In some cases, there is only one kind of domain mismatch like additive noise or reverberation, but in many cases, there are more than one distortion. Finding a solution for domain adaptation in the presence of different distortions is a challenge. In this paper we investigate the situation in which there is none, one or more of the following distortions: early reverberation, full reverberation, additive noise. We propose two configurations to compensate for these distortions. In the first one a specific denoising autoencoder is used for each distortion. In the second configuration, a denoising autoencoder is used to compensate for all of these distortions simultaneously. Our experiments show that, in the co-existence of noise and reverberation, the second configuration gives better results. For example, with the second configuration we obtained 76.6% relative improvement of EER for utterances longer than 12 seconds. For other situations in the presence of only one distortion, the second configuration gives almost the same results achieved by using a specific model for each distortion.

关键词： additive noise early reverberation full reverberation x-vector denoising autoencoder

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MULTI-TASK autoencoder FOR NOISE-ROBUST SPEECH RECOGNITION

MULTI-TASK AUTOENCODER FOR NOISE-ROBUST SPEECH RECOGNITION

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhang, Haoyi Liu, Conggui Inoue, Nakamasa Shinoda, Koichi Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781538646588

For speech recognition in noisy environments, we propose a multi-task autoencoder which estimates not only clean speech features but also noise features from noisy speech. We introduce the deSpeeching autoencoder, which excludes speech signals from noisy speech, and combine it with the conventional denoising autoencoder to form a unified multi-task autoencoder (MTAE). We evaluate it using the Aurora 2 dataset and CHIME 3 dataset. It reduced WER by 15.7% from the conventional denoising autoencoder in the Aurora 2 test set A.

关键词： denoising autoencoder deSpeeching autoencoder robust speech recognition

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Mapping Between Ultrasound and Vowel Speech Using DNN Framework 9

Mapping Between Ultrasound and Vowel Speech Using DNN Framew...

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9th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Zheng, Xinyuan Wei, Jianguo Lu, Wenhuan Fang, Qiang Dang, Jianwu Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China Chinese Acad Sci Beijing 100864 Peoples R China JAIST Sch Informat Tokyo Japan

ISBN: (纸本)9781479942190

Building up the mapping between articulatory movements and corresponding speech could great facility the speech training and speech aid for voiceless patients. In this paper, we propose a deep learning framework for building up a mapping between articulatory information and corresponding speech, which were recorded by ultrasound system. The dataset includes six Chinese vowels. We use Bimodal Deep autoencoder algorithm based on RBM to learn the relationship between speech and articulation, the weights matrix of representation of them. Speech and ultrasound images have been reconstructed using the extracted features. The reconstruction error of articulation by our method is less than that of PCA based approach. The reconstructed speech is similar to the original one. We propose a mapping from ultrasound tongue image to acoustic signal with a revised denoising autoencoder, the results show that it is a promising approach. In contrast, another experiment is conducted to synthesize the ultrasound tongue image from the speech, but the result should be improved.

关键词： DBN articulatory-acoustic mapping denoising autoencoder

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Align-Denoise: Single-Pass Non-Autoregressive Speech Recognition 22

Align-Denoise: Single-Pass Non-Autoregressive Speech Recogni...

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Interspeech Conference

作者： Chen, Nanxin Zelasko, Piotr Moro-Velazquez, Laureano Villalba, Jesus Dehak, Najim Johns Hopkins Univ Ctr Language & Speech Proc Baltimore MD 21218 USA Johns Hopkins Univ Human Language Technol Ctr Excellence Baltimore MD 21218 USA

ISBN: (纸本)9781713836902

Deep autoregressive models start to become comparable or superior to the conventional systems for automatic speech recognition. However, for the inference computation, they still suffer from inference speed issue due to their token-by-token decoding characteristic. Non-autoregressive models greatly improve decoding speed by supporting decoding within a constant number of iterations. For example, Align-Refine was proposed to improve the performance of the non-autoregressive system by refining the alignment iteratively. In this work, we propose a new perspective to connect Align-Refine and denoising autoencoder. We introduce a novel noisy distribution to sample the alignment directly instead of obtaining it from the decoder output. The experimental results reveal that the proposed Align-Denoise speeds up both training and inference with performance improvement up to 5% relatively using single-pass decoding.

关键词： speech recognition non-autoregressive model deep learning denoising autoencoder iterative refinement

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Reading Imagined Letter Shapes from the Mind's Eye Using Real-time 7 Tesla fMRI 10

Reading Imagined Letter Shapes from the Mind's Eye Using Rea...

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10th International Winter Conference on Brain-Computer Interface (BCI)

作者： Goebel, Rainer van Hoof, Rick Bhat, Salil Luhrs, Michael Senden, Mario Maastricht Univ Dept Cognit Neurosci Maastricht Netherlands

ISBN: (纸本)9781665413374

We present a 7 Tesla fMRI proof-of-concept study of the first letter speller BCI that decodes imagined letter shapes from activity patterns in early visual cortical areas. New tools are developed to enable real-time population receptive field retinotopic mapping for encoding and decoding. Using two different letter shapes (H and T), classification performance of generated activity patterns during imagery reaches 80% accuracy in each individual. Using a denoising autoencoder, recognizable letter shapes could be reconstructed and displayed as feedback to participants in the scanner.

关键词： fMRI letter speller imagery population receptive fields denoising autoencoder

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Research on Anomaly Detection of Offshore Buoy Water Temperature Data Based on LSTM-DAE Model 5

Research on Anomaly Detection of Offshore Buoy Water Tempera...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Gao, Saiyu Song, Miaomiao Hu, Wei Li, Wenqing Zheng, Shanshan Zhuo, Lei Liu, Ru Huang, Jiuzhang Song, Haihui Cheng, Kaiyu Institute of Oceanographic Instrumentation Qilu University of Technology Shandong Academy of Sciences Qingdao266001 China National Marine Monitoring Equipment Engineering Technology Research Center China

ISBN: (纸本)9798350357950

At present, there are many quality problems in the hydrological data of ocean buoys, and it is urgent to carry out scientific and standardized data quality control to ensure the accuracy and rationality of the buoy data. This paper focuses on water temperature data collected by offshore buoys, and an LSTM-DAE abnormal value detection model is proposed. In this model, both the encoder and decoder of the denoising autoencoder adopt a long short-term memory network (LSTM) as the core component of the neural network layer. Experimental results show that compared with the traditional LSTM model for outlier detection, the proposed model can better deal with the long-term series characteristics in the buoy data and reconstruct the input data more accurately, so as to improve the effect of outlier detection. © 2023 IEEE.

关键词： abnormal value denoising autoencoder LSTM ocean buoys quality control

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Modulation Signal denoising Based on Auto-encoder 16

Modulation Signal Denoising Based on Auto-encoder

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16th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： Mo, Zunyin Li, Hongli Wang, Jiao Huang, Hao Li, Jianqing Univ Elect Sci & Technol China Sch Phys Chengdu Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9781665449083

This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of the modulated signal with noise as the input of the auto-encoder and the features of the clean signal as the label. At the same time, back-propagation algorithm and gradient descent method are used to optimize and update the parameters in the auto-encoder model to minimize the reconstruction error, so as to realize the denoising function of the modulated signal. For a variety of modulation types, this method can improve the modulation signal about 3-9 dB in different SNR environment. The denoising model can generate high-level features of different modulation signals without any artificial feature extraction and prior knowledge and has strong feature representation ability. It has the advantages of strong versatility, low complexity, good denoising effect and good stability.

关键词： denoising autoencoder Machine learning for communications Advanced signal processing for transmission convolution neural network

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OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS

OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DA...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Jo, Inhyuk Kim, Jungtaek Kang, Hyohyeong Kim, Yong-Deok Choi, Seungjin POSTECH Dept Comp Sci & Engn Pohang South Korea Samsung Elect Software R&D Ctr Device Solut Seoul South Korea

ISBN: (纸本)9781538646588

We present a new method to generate fake data in unknown classes in generative adversarial networks (GANs) framework. The generator in GANs is trained to generate somewhat similar to data in known classes but the different one by modelling noisy distribution on feature space of a classifier using proposed marginal denoising autoencoder. The generated data are treated as fake instances in unknown classes and given to the classifier to make it be robust to the real unknown classes. Our results show that synthetic data can act as fake unknown classes and keep down the certainty of the classifier on real unknown classes meanwhile the classification capability of known classes is not degenerated, even improved.

关键词： Generative adversarial networks denoising autoencoder Open set recognition Feature matching

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Self-supervised Siamese autoencoders 22nd

Self-supervised Siamese Autoencoders

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22nd International Symposium on Intelligent Data Analysis (IDA)

作者： Baier, Friederike Mair, Sebastian Fadel, Samuel G. Leuphana Univ Luneburg Luneburg Germany Uppsala Univ Uppsala Sweden Linkoping Univ Linkoping Sweden

ISBN: (纸本)9783031585463;9783031585470

In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the same or even higher downstream performance. The goal is to pre-train deep neural networks on a self-supervised task, making them able to extract meaningful features from raw input data afterwards. Previously, autoencoders and Siamese networks have been successfully employed as feature extractors for tasks such as image classification. However, both have their individual shortcomings and benefits. In this paper, we combine their complementary strengths by proposing a new method called SidAE (Siamese denoising autoencoder). Using an image classification downstream task, we show that our model outperforms two self-supervised baselines across multiple data sets and scenarios. Crucially, this includes conditions in which only a small amount of labeled data is available. Empirically, the Siamese component has more impact, but the denoising autoencoder is nevertheless necessary to improve performance.

关键词： Self-supervised learning representation learning Siamese networks denoising autoencoder pre-training image classification

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