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检索条件"主题词=variational autoencoder"

共 1535 条记录，以下是251-260 订阅

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Black-Box Audio Adversarial Example Generation Using variational autoencoder 23rd

Black-Box Audio Adversarial Example Generation Using Variati...

引用

23rd International Conference on Information and Communications Security (ICICS)

作者： Zong, Wei Chow, Yang-Wai Susilo, Willy Univ Wollongong Sch Comp & Informat Technol Inst Cybersecur & Cryptol Wollongong NSW Australia

ISBN: (纸本)9783030880521;9783030880514

Automatic speech recognition (ASR) applications are ubiquitous these days. A variety of commercial products utilize powerful ASR capabilities to transcribe user speech. However, as with other deep learning models, the techniques underlying ASR models suffer from adversarial example (AE) attacks. Audio AEs resemble non-suspicious audio to the casual listener, but will be incorrectly transcribed by an ASR system. Existing black-box AE techniques require excessive requests sent to a targeted system. Such suspicious behavior can potentially trigger a threat alert on the system. This paper proposes a method of generating black-box AEs in a way that significantly reduces the required amount of requests. We describe our proposed method and presents experimental results demonstrating its effectiveness in generating word-level and sentence-level AEs that are incorrectly transcribed by an ASR system.

关键词： Adversarial example Automatic speech recognition Deep learning Machine learning variational autoencoder

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A Method to Inspect the Implementation of Electricity Price Based on Deep Learning variational autoencoder 2

A Method to Inspect the Implementation of Electricity Price ...

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2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Gao, Xiying Ye, Ning Song, Jinchun Wang, Haomiao Zhang, Ye Guan, Yan Song, Xiaowen Cai, Yingkai Zhang, Wenshu Hui, Qian Li, Dan State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Mkt Dept Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China State Grid Liaoyang Elect Power B&E Off Liaoyang Peoples R China

ISBN: (纸本)9781538685495

In this paper, we propose a method for performing electricity price execution inspection by using a variational autoencoder technology in deep learning. The variational autoencoder based anomaly detection algorithm(VABAD) can be used both as a discriminant model and as a feature of the generation model, which effectively solves the calculation problem of multiple heterogeneous parameters of current electricity price inspection implementation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. It is used by autoencoder based anomaly detection methods. Experimental results show that the proposed method has been validated and compared to the existing approaches. The databases used in this paper come from Power Marketing System that occurred in Liaoning, China in 2015.

关键词： deep learning variational autoencoder electricity price inspection implementation data heterogeneity

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A BAYESIAN PERMUTATION TRAINING DEEP REPRESENTATION LEARNING METHOD FOR SPEECH ENHANCEMENT WITH variational autoencoder 47

A BAYESIAN PERMUTATION TRAINING DEEP REPRESENTATION LEARNING...

引用

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Xiang, Yang Hojvang, Jesper Lisby Rasmussen, Morten Hojfeldt Christensen, Mads Grasboll Aalborg Univ Audio Anal Lab CREATE Aalbory Denmark Capturi AS Aarhus Denmark

ISBN: (纸本)9781665405409

Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, current VAE-based SE methods only apply VAE to model speech signal, while noise is modeled using the traditional non-negative matrix factorization (NMF) model. One of the most important reasons for using NMF is that these VAE-based methods cannot disentangle the speech and noise latent variables from the observed signal. Based on Bayesian theory, this paper derives a novel variational lower bound for VAE, which ensures that VAE can be trained in supervision, and can disentangle speech and noise latent variables from the observed signal. This means that the proposed method can apply the VAE to model both speech and noise signals, which is totally different from the previous VAE-based SE works. More specifically, the proposed DRL method can learn to impose speech and noise signal priors to different sets of latent variables for SE. The experimental results show that the proposed method can not only disentangle speech and noise latent variables from the observed signal, but also obtain a higher scale-invariant signal-to-distortion ratio and speech quality score than the similar deep neural network-based (DNN) SE method.

关键词： Deep representation learning speech enhancement Bayesian permutation training variational autoencoder

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Emotion-Guided Music Accompaniment Generation Based on variational autoencoder

Emotion-Guided Music Accompaniment Generation Based on Varia...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Qi Zhang, Shubing Zhou, Li China Univ Geosci Wuhan Peoples R China

ISBN: (纸本)9781665488679

Music accompaniment generation is a crucial aspect in the composition process. Deep neural networks have made significant strides in this field, but it remains a challenge for AI to effectively incorporate human emotions to create beautiful accompaniments. Existing models struggle to effectively characterize human emotions within neural network models while composing music. To address this issue, we propose the use of an easy-to-represent emotion flow model, the Valence/Arousal Curve, which allows for the compatibility of emotional information within the model through data transformation and enhances interpretability of emotional factors by utilizing a variational autoencoder as the model structure. Further, we used relative self-attention to maintain the structure of the music at music phrase level and to generate a richer accompaniment when combined with the rules of music theory. Our experimental results indicate that the emotional flow of the music generated by our model has a strong correlation with the input emotion, demonstrating the model's strong interpretability and control of emotional flow. The generated music is also well-structured, diverse, and dynamic, outperforming the baseline models.

关键词： Music Accompaniment Generation Emotional Flow variational autoencoder Rule constraints

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Unsupervised Anomaly detection of LM Guide Using variational autoencoder 11

Unsupervised Anomaly detection of LM Guide Using Variational...

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11th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Kim, Min Su Yun, Jong Pil Lee, Suwoong Park, PooGyeon Pohang Univ Sci & Technol Pohang South Korea Korea Inst Ind Technol Cheonan Si South Korea

ISBN: (纸本)9781479975143

Linear Motion (LM) is a linear motion guide that helps directional moving of machine. It is important to judge the anomaly state of LM guides because LM guides are used in various industries to support various task in industry application. In this paper, we proposed a machine learning algorithm for determining the anomaly state of LM guide. Considering that it is difficult to actually generate the anomaly signal, we trained model with only healthy state data. One of the generative models, variational autoencoder, is used for training healthy state data and the distribution of healthy state data is trained. Our trained model determines whether or not anomaly state has occurred based on a reconstruction error of the trained network.

关键词： LM guide Anomaly detection variational autoencoder

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A variational autoencoder Approach for Speech Signal Separation 12th

A Variational Autoencoder Approach for Speech Signal Separat...

引用

12th International Conference on Computational Collective Intelligence (ICCCI)

作者： Do, Hao D. Tran, Son T. Chau, Duc T. Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam OLLI Technol JSC Ho Chi Minh City Vietnam

ISBN: (纸本)9783030630065;9783030630072

Speech separation plays an important role in a speech-related system since it can denoise, extract, and enhance speech signals. In recent years, many methods are proposed to separate the human voice of noise and other sounds. To separate the speech from a complicated signal, we propose a more powerful method by using a VAE model and then postprocessing with a bandpass filter. This combination can use to extract the original human speech in the mixture with not only high-frequency noise but also many different sounds. Our approach can be flexibly applied for the new background sounds.

关键词： Bandpass filter variational autoencoder Frequency domain Speech separation

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Vector -Quantization variational autoencoder Based Data Rate Reduction for Wireless Ultrasound Imaging Systems

Vector -Quantization Variational Autoencoder Based Data Rate...

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IEEE SoutheastCon Conference on Engineering the Future

作者： Bastola, Sulav Tekes, Coskun Kennesaw State Univ Elect & Comp Engn Marietta GA 30060 USA

ISBN: (纸本)9798350317107;9798350317114

Ultrasound imaging has become a preferred medical diagnostics tool for many applications due to its cost-effectiveness, non-ionizing nature, and real-time capabilities. There has been a significant progress in the development of new ultrasound probes and systems, particularly portable and wearable devices, incorporating new transducer technologies, sophisticated electronics integration, artificial intelligence and advanced beamforming strategies. Wearable ultrasound systems, equipped with wireless data transfer interfaces, offer unique advantages for continuous signal monitoring of the patients for their critical conditions both in and out-of-hospital settings. Many challenges specifically in data rate reduction for wireless real-time systems needs to be explored. To address this issue, in this paper, we present a vector quantized variational autoencoder model to effectively compress ultrasound RF signals without compromising image quality. We tested and evaluated the performance of the model on real ultrasound datasets. The experimental results demonstrate 92% of data reduction enabling achievable real-time imaging speeds over wireless channels.

关键词： Ultrasound imaging Data reduction variational autoencoder wearable ultrasound

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INTEGRATION OF variational autoencoder AND SPATIAL CLUSTERING FOR ADAPTIVE MULTI-CHANNEL NEURAL SPEECH SEPARATION

INTEGRATION OF VARIATIONAL AUTOENCODER AND SPATIAL CLUSTERIN...

引用

IEEE Spoken Language Technology Workshop (SLT)

作者： Zmolikova, Katerina Delcroix, Marc Burget, Lukas Nakatani, Tomohiro Cernocky, Jan Honza Brno Univ Technol Fac IT IT4I Ctr Excellence Brno Czech Republic NTT Corp NTT Commun Sci Labs Kyoto Japan

ISBN: (纸本)9781728170664

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integrating spatial clustering with a spectral model was shown in several works. As the spectral model, previous works used either factorial generative models of the mixed speech or discriminative neural networks. In our work, we combine the strengths of both approaches, by building a factorial model based on a generative neural network, a variational autoencoder. By doing so, we can exploit the modeling power of neural networks, but at the same time, keep a structured model. Such a model can be advantageous when adapting to new noise conditions as only the noise part of the model needs to be modified. We show experimentally, that our model significantly outperforms previous factorial model based on Gaussian mixture model (DOLPHIN), performs comparably to integration of permutation invariant training with spatial clustering, and enables us to easily adapt to new noise conditions.

关键词： Multi-channel speech separation variational autoencoder spatial clustering DOLPHIN

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Effect of Integrating variational autoencoder with SENet on Dimensionality Reduction in Driving Behavior Learning 12

Effect of Integrating Variational Autoencoder with SENet on ...

引用

12th International Symposium on Computing and Networking Workshops

作者： Uehara, Yuta Matumae, Susumu Saga Univ Dept Informat Sci Saga Japan

ISBN: (纸本)9798331505356;9798331505349

Dimensionality reduction using variational autoencoder (VAE) is widely employed in learning diverse state representations, such as in autonomous driving tasks. Conventional VAE-based dimensionality reduction is a commonly used method for reducing the computational cost associated with learning from high-dimensional data, particularly image data, while achieving high performance. In this paper, we investigate the impact of integrating VAE with Squeeze-and-Excitation Networks (SENet), referred to as SENet-VAE, on the accuracy of learning driving behaviors in deep reinforcement learning. We conduct a series of experiments comparing three setups: raw image data, conventional VAE, and SENet-VAE. Additionally, we explore the effect of applying hyperparameters to the Kullback-Leibler (KL) divergence term in the objective function of the SENet-VAE to further optimize performance. Our results demonstrate that SENet-VAE outperforms the conventional VAE in terms of learning accuracy, with hyperparameter tuning leading to performance gains.

关键词： Squeeze-and-Excitation Network variational autoencoder Dimensionality Reduction Autonomous Driving

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Unsupervised classification of high-dimension and low-sample data with variational autoencoder based dimensionality reduction 4

Unsupervised classification of high-dimension and low-sample...

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4th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Mahmud, Mohammad Sultan Fu, Xianghua Shenzhen Univ Coll Comp & Software Engn Big Data Inst Shenzhen 518060 Peoples R China Shenzhen Technol Univ Fac Arts & Sci Shenzhen 518118 Peoples R China

ISBN: (纸本)9781728100647

In data mining research and development, one of the defining challenges is to perform classification or clustering tasks for relatively limited-samples with high-dimensions data, also known as high-dimensional limited-sample size (HDLSS) problem. Due to the limited-sample-size, there is a lack of enough training data to train classification models. Also, the `curse of dimensionality' aspect is often a restriction on the effectiveness of many methods for solving HDLSS problem. Classification model with limited-sample dataset lead to overfitting and cannot achieve a satisfactory result. Thus, the unsupervised method is a better choice to solve such problems. Due to the emergence of deep learning, their plenty of applications and promising outcome, it is required an extensive analysis of the deep learning technique on HDLSS dataset. This paper aims at evaluating the performance of variational autoencoder (VAE) based dimensionality reduction and unsupervised classification on the HDESS dataset. The performance of VAE is compared with two existing techniques namely PCA and NMF on fourteen datasets in term of three evaluation metrics namely purity, Rand index, and NMI. The experimental result shows the superiority of VAE over the traditional methods on the HDLSS dataset.

关键词： HDLSS dataset dimensionality reduction variational autoencoder unsupervised classification

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