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Audiovisual speech recognition based on a deep convolutional neural network 认领 引用 被引量：2
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作者 Shashidhar Rudregowda Sudarshan Patilkulkarni +2 位作者 Vinayakumar Ravi Gururaj H.L. Moez Krichen 《Data Science and Management》 2024年第1期25-34,共10页
Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for India... Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual recognition.Audio features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural network.Visual feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural networks.Finally,integration was performed using a deep convolutional network.The audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 epochs.The training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 epochs.After integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively. 展开更多
关键词 Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)
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深度学习农作物分类的弱样本适用性 认领 引用 被引量：13
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作者 许晴 张锦水 +3 位作者 张凤 盖爽 杨志 段雅鸣 《遥感学报》 EI CSCD 北大核心 2022年第7期1395-1409,共15页
基于大数据驱动的深度学习挖掘图像数据的规律和层次已成为遥感影像解译的研究热点。海量标签样本是训练深度学习模型的前提条件,但成本昂贵的人工标记样本限制了深度学习技术在遥感领域的应用。本文提出了一种基于弱样本的深度学习模... 基于大数据驱动的深度学习挖掘图像数据的规律和层次已成为遥感影像解译的研究热点。海量标签样本是训练深度学习模型的前提条件,但成本昂贵的人工标记样本限制了深度学习技术在遥感领域的应用。本文提出了一种基于弱样本的深度学习模型农作物分类策略:以GF-1影像为数据源,将传统分类器SVM分类结果视为弱样本,训练深度卷积网络模型DCNN(Deep Convolutional Neural Networks),获取辽宁省水稻和玉米的空间分布,分析弱样本的适用性。结果显示:测试集总体精度达到0.90,水稻和玉米F1分数分别为0.81和0.90;在不同地形地貌、复杂种植结构的农业景观下均表现出良好的分类效果;与SVM结果的空间一致性为0.90;当弱样本最大面积误差比例小于0.36时,弱样本仍适用于DCNN作物分类,结果的总体精度保持在0.86以上。综上,该策略一定程度上消除了深度学习模型对大量人工标记样本高度依赖的局限性,为实现大尺度农作物遥感分类提供了一种新途径。 展开更多
关键词 弱样本 卷积神经网络模型 深度学习 GF-1影像 农作物遥感分类
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Fault Diagnosis for Wind Turbine Flange Bolts Based on One-Dimensional Depthwise Separable Convolutions 认领 引用
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作者 Yongchao Liu Shuqing Dong +3 位作者 Qingfeng Wang Wenhe Cai Ruizhuo Song Qinglai Wei 《The International Journal of Intelligent Control and Systems》 2024年第1期42-47,共6页
In this paper,a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable convolutions.The main idea is to use a one-dimen... In this paper,a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable convolutions.The main idea is to use a one-dimensional convolutional neural network model to classify and identify the acoustic vibration signals of bolts,which represent different bolt damage states.Through the methods of knock test and modal simulation,it is concluded that the damage state of wind turbine flange bolt is related to the natural frequency distribution of acoustic vibration signal.It is found that the bolt damage state affects the modal shape of the structure,and then affects the natural frequency distribution of the bolt vibration signal.Therefore,the damage state can be obtained by identifying the natural frequency distribution of the bolt acoustic vibration signal.In the present one-dimensional depth-detachable convolutional neural network model,the one-dimensional vector is first convolved into multiple channels,and then each channel is separately learned by depth-detachable convolution,which can effectively improve the feature quality and the effect of data classification.From the perspective of the realization mechanism of convolution operation,the depthwise separable convolution operation has fewer parameters and faster computing speed,making it easier to build lightweight models and deploy them to mobile devices. 展开更多
关键词 Wind turbine flange bolts one-dimensional convolutional neural network(1DCNN)model depthwise separable convolutions damage identification
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