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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是251-260 订阅

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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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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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Emotion-Guided Music Accompaniment Generation Based on variational autoencoder

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

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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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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 ...

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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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Deep variational autoencoder: an efficient tool for PHM frameworks.

Deep Variational Autoencoder: an efficient tool for PHM fram...

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Prognostics and System Health Management Conference (PHM-Besancon)

作者： Zemouri, Ryad Levesque, Melanie Amyot, Normand Hudon, Claude Kokoko, Olivier HESAM Univ Cedr Lab CNAM Paris France Inst Rech Hydroquebec IREQ Varennes PQ J3X 1S1 Canada

ISBN: (纸本)9781728156750

Deep learning (DL) has been recently used in several applications of machine health monitoring systems. Unfortunately, most of these DL models are considered as black-boxes with low interpretability. In this research, we propose an original PHM framework based on visual data analysis. The most suitable space dimension for the data visualization is the 2D-space, which necessarily involves a significant reduction from a high-dimensional to a low-dimensional data space. To perform the data analysis and the diagnostic interpretation in a PHM framework, a variational autoencoder (VAE) is used jointly with a classifier. The proposed model was evaluated to automatically recognize individual Partial Discharge (PD) sources for hydro generators monitoring.

关键词： variational autoencoder data visualization system health management diagnosis analysis

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DISCOURSE-LEVEL PROSODY MODELING WITH A variational autoencoder FOR NON-AUTOREGRESSIVE EXPRESSIVE SPEECH SYNTHESIS 47

DISCOURSE-LEVEL PROSODY MODELING WITH A VARIATIONAL AUTOENCO...

引用

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

作者： Wu, Ning-Qian Liu, Zhao-Ci Ling, Zhen-Hua Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Peoples R China

ISBN: (纸本)9781665405409

To address the issue of one-to-many mapping from phoneme sequences to acoustic features in expressive speech synthesis, this paper proposes a method of discourse-level prosody modeling with a variational autoencoder (VAE) based on the non-autoregressive architecture of FastSpeech. In this method, phone-level prosody codes are extracted from prosody features by combining VAE with Fast-Speech, and are predicted using discourse-level text features together with BERT embeddings. The continuous wavelet transform (CWT) in FastSpeech2 for F0 representation is not necessary anymore. Experimental results on a Chinese audiobook dataset show that our proposed method can effectively take advantage of discourse-level linguistic information and has outperformed FastSpeech2 on the naturalness and expressiveness of synthetic speech.

关键词： speech synthesis prosody modeling FastSpeech discourse-level modeling variational autoencoder

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A Study on Developmental Artificial Neural Networks that Integrate Multiple Functions using variational autoencoder 10

A Study on Developmental Artificial Neural Networks that Int...

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10th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Iwai, Haruka Kobayashi, Ichiro Ochanomizu Univ Grad Sch Humanities & Sci Adv Sci Tokyo Japan

ISBN: (纸本)9798350359374

In conventional deep learning fine tuning or transfer learning, the synaptic weights of neural networks that have been previously learned and acquired in a particular task solution are transferred to the learning of a similar task to achieve efficient learning of that task. In this case, both tasks are assumed to be similar, and the function to be transferred to solve a task is a single one. On the other hand, organisms can abstract multiple experiences acquired in the past and adapt them as knowledge to new tasks. This requires a new neural network architecture that fuses neural networks that have learned individual problem-solving functions and is capable of transfer learning of multiple functions. To acquire such an architecture, we aim to develop a framework to fuse neural networks with multiple individual functions to acquire a new network topology. Based on this, in this study, we develop a Developmental Artificial Neural Networks (DANN) capable of simultaneously transferring multiple functions that generate a neural network with new functions that integrate multiple functions. we use the framework of Weight Agnostic Neural Networks (WANN), in which the topology is acquired evolutionarily rather than by the synaptic weight of the networks, and the obtained topology is transformed into a matrix by using an embedded representation of the generation rules, which is then used as input to the encoder-decoder model based on Grammar variational autoencoder (GVAE). Through the experimental results, it was confirmed that multiple neural network models that solve individual tasks can be represented in the latent space, and that capturing the relationships among tasks.

关键词： AutoML Transfer Learning Weight Agnostic Neural Networks variational autoencoder

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High-Dimensional Analog Circuit Sizing via Bayesian Optimization in the variational autoencoder Enhanced Latent Space

High-Dimensional Analog Circuit Sizing via Bayesian Optimiza...

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2nd International Symposium of Electronics Design Automation (ISEDA)

作者： Li, Wangzhen Bi, Zhaori Zeng, Xuan Fudan Univ Microelect Dept State Key Lab ASIC & Syst Shanghai Peoples R China

ISBN: (纸本)9798350352047;9798350352030

High-dimensional analog circuit sizing with machine learning-based surrogate models suffers from the high sampling cost of evaluating expensive black-box objective functions in huge design spaces. This work addresses the sampling efficiency challenge by elaborately reducing the dimensionality of the input spaces, enabling efficient optimization for automated analog circuit sizing. We propose a latent space optimization method that includes an iteratively updated generative model based on a variational autoencoder to embed the solution manifold of analog circuits to a low-dimensional and continuous space, where the latent variables are optimized using Bayesian optimization. The effectiveness of the proposed method has been verified on two real-world analog circuits with 18 and 59 design variables. In comparison with BO in the original high-d spaces or latent low-d spaces assisted by other embedding strategies, the proposed method achieves 23%(similar to)73% improvements in optimization performance within the same runtime limitations. We also conduct a technology migration experiment using the pre-trained variational autoencoder model, which demonstrates the necessity of pre-training and the scalability of the proposed method.

关键词： analog circuit sizing sampling efficiency Bayesian optimization variational autoencoder latent space

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