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学者姓名：李应
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针对低信噪比下的声音事件检测问题,提出基于能量压缩和灰度增强的多频带能量分布图的声音事件检测方法。将声音数据的gammatone频谱转成能量谱,对不同频带的能量进行不同比例的能量压缩,计算其多频带能量分布图,并对其进行灰度增强;对调整后的多频带能量分布图进行8×8的分块,对每一子块进行奇异值分解,提取主要数值作为声音事件的特征;利用随机森林分类器对特征建模与检测。实验结果表明,在低信噪比环境下,该方法具有良好的检测效果。
Keyword ：
声音事件检测 声音事件检测 多频带能量分布 多频带能量分布 奇异值 奇异值 灰度增强 灰度增强 能量压缩 能量压缩 随机森林 随机森林
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| GB/T 7714 | 林艺明 , 李应 . 利用能量压缩后的MBPD检测低信噪比声音事件 [J]. | 计算机应用与软件 , 2021 , 38 (06) : 126-133 . |
| MLA | 林艺明 等. "利用能量压缩后的MBPD检测低信噪比声音事件" . | 计算机应用与软件 38 . 06 (2021) : 126-133 . |
| APA | 林艺明 , 李应 . 利用能量压缩后的MBPD检测低信噪比声音事件 . | 计算机应用与软件 , 2021 , 38 (06) , 126-133 . |
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Environmental sound classification (ESC) is an important but challenging issue. In this paper, we propose a new deep convolutional neural network, which uses concatenated spectrogram as input features, for ESC task. This concatenated spectrogram feature we adopt can increase the richness of features compared with single spectrogram. It is generated by concatenating two regular spectrograms, the Log-Mel spectrogram and the Log-Gammatone spectrogram. The network we propose uses convolutional blocks to extract and derive high-level feature images from concatenated spectrogram, and each block is composed of three convolutional layers and a pooling layer. In order to keep depth of the network and reduce numbers of parameters, we use filter with a small receptive field in each convolutional layer. Besides, we use the average pooling to keep more information. Our method was tested on ESC-50 and UrbanSound8K and achieved classification accuracy of 83.8% and 80.3%, respectively. The experimental results show that the proposed method is suitable for ESC task. © 2019 IEEE.
Keyword ：
Computer networks Computer networks Convolution Convolution Convolutional neural networks Convolutional neural networks Deep neural networks Deep neural networks Spectrographs Spectrographs
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| GB/T 7714 | Chi, Zhejian , Li, Ying , Chen, Cheng . Deep Convolutional Neural Network Combined with Concatenated Spectrogram for Environmental Sound Classification [C] . 2019 : 251-254 . |
| MLA | Chi, Zhejian 等. "Deep Convolutional Neural Network Combined with Concatenated Spectrogram for Environmental Sound Classification" . (2019) : 251-254 . |
| APA | Chi, Zhejian , Li, Ying , Chen, Cheng . Deep Convolutional Neural Network Combined with Concatenated Spectrogram for Environmental Sound Classification . (2019) : 251-254 . |
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该文针对低信噪比噪声环境下的声音事件检测问题,提出基于多频带能量分布图离散余弦变换的声音事件检测的方法。首先,将声音数据转化为gammatone频谱,并计算其多频带能量分布;接着,对多频带能量分布图进行8×8分块与离散余弦变换;然后,对8×8的离散余弦变换系数进行Zigzag扫描,抽取离散余弦变换系数的主要系数作为声音事件的特征;最后,利用随机森林分类器对特征建模与检测。实验结果表明,在低信噪比及各种噪声环境下,该文提出的方法具有良好的检测效果。
Keyword ：
声音事件检测 声音事件检测 多频带能量分布 多频带能量分布 离散余弦变换 离散余弦变换 随机森林 随机森林
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| GB/T 7714 | 李应 , 吴灵菲 . 用多频带能量分布检测低信噪比声音事件 [J]. | 电子与信息学报 , 2018 , 40 (12) : 2905-2912 . |
| MLA | 李应 等. "用多频带能量分布检测低信噪比声音事件" . | 电子与信息学报 40 . 12 (2018) : 2905-2912 . |
| APA | 李应 , 吴灵菲 . 用多频带能量分布检测低信噪比声音事件 . | 电子与信息学报 , 2018 , 40 (12) , 2905-2912 . |
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论文针对各种背景声音中低信噪比声音事件的检测问题,提出把背景声音与声音事件混合,形成带噪声样本来训练分类器.在预处理阶段,使用基于经验模态分解与2-6级固有模态函数的投票方法,对背景声音与声音事件端点进行预测并估算信噪比.接着使用子带能量分布方法,提取声音数据的特征.最后,论文将背景声音与声音事件样本库中所有声音样本按照估算的信噪比相混合,生成混合声音特征训练多随机森林,用于低信噪比声音事件的检测.实验证实,所提出的方法可以用于各种声场景下低信噪比声音事件的检测,并能在信噪比为-5dB的情况下保持67. 1%的平均检测率.
Keyword ：
信噪比 信噪比 声音事件检测 声音事件检测 子带能量分布 子带能量分布 经验模态分解 经验模态分解 随机森林 随机森林
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| GB/T 7714 | 李应 , 印佳丽 . 基于多随机森林的低信噪比声音事件检测 [J]. | 电子学报 , 2018 , 46 (11) : 2705-2713 . |
| MLA | 李应 等. "基于多随机森林的低信噪比声音事件检测" . | 电子学报 46 . 11 (2018) : 2705-2713 . |
| APA | 李应 , 印佳丽 . 基于多随机森林的低信噪比声音事件检测 . | 电子学报 , 2018 , 46 (11) , 2705-2713 . |
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Intelligent vehicles use advanced driver assistance systems (ADASs) to mitigate driving risks. There is increasing demand for an ADAS framework that can increase driving safety by detecting dangerous driving behavior from driver, vehicle, and lane attributes. However, because dangerous driving behavior in real-world driving scenarios can be caused by any or a combination of driver, vehicle, and lane attributes, the detection of dangerous driving behavior using conventional approaches that focus on only one type of attribute may not be sufficient to improve driving safety in realistic situations. To facilitate driving safety improvements, the concept of dangerous driving intensity (DDI) is introduced in this paper, and the objective of dangerous driving behavior detection is converted into DDI estimation based on the three attribute types. To this end, we propose a framework, wherein fuzzy sets are optimized using particle swarm optimization for modeling driver, vehicle, and lane attributes and then used to accurately estimate the DDI. The mean opinion scores of experienced drivers are employed to label DDI for a fair comparison with the results of our framework. The experimental results demonstrate that the driver, vehicle, and lane attributes defined in this paper provide useful cues for DDI analysis; furthermore, the results obtained using the framework are in favorable agreement with those obtained in the perception study. The proposed framework can greatly increase driving safety in intelligent vehicles, where most of the driving risk is within the control of the driver.
Keyword ：
advanced driver assistance systems advanced driver assistance systems dangerous intensity dangerous intensity Driving behaviors Driving behaviors
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| GB/T 7714 | Yin, Jia-Li , Chen, Bo-Hao , Lai, Kuo-Hua Robert et al. Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals [J]. | IEEE SENSORS JOURNAL , 2018 , 18 (12) : 4785-4794 . |
| MLA | Yin, Jia-Li et al. "Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals" . | IEEE SENSORS JOURNAL 18 . 12 (2018) : 4785-4794 . |
| APA | Yin, Jia-Li , Chen, Bo-Hao , Lai, Kuo-Hua Robert , Li, Ying . Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals . | IEEE SENSORS JOURNAL , 2018 , 18 (12) , 4785-4794 . |
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Impulse noise corruption in digital images frequently occurs because of errors generated by noisy sensors or communication channels, such as faulty memory locations in devices, malfunctioning pixels within a camera, or bit errors in transmission. Although recently developed big data streaming enhances the viability of video communication, visual distortions in images caused by impulse noise corruption can negatively affect video communication applications. In addition, sparsity, density, and multimodality in large volumes of noisy images have often been ignored in recent studies, whereas these issues have become important because of the increasing viability of video communication services. To effectively eliminate the visual effects generated by the impulse noise from the corrupted images, this study proposes a novel model that uses a devised cost function involving semisupervised learning based on a large amount of corrupted image data with a few labeled training samples. The proposed model qualitatively and quantitatively outperforms the existing state-of-the-art image reconstruction models in terms of the denoising effect.
Keyword ：
big image data big image data Noise removal Noise removal semisupervised learning semisupervised learning
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| GB/T 7714 | Yin, Jia-Li , Chen, Bo-Hao , Li, Ying . Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2018 , 20 (11) : 3045-3056 . |
| MLA | Yin, Jia-Li et al. "Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data" . | IEEE TRANSACTIONS ON MULTIMEDIA 20 . 11 (2018) : 3045-3056 . |
| APA | Yin, Jia-Li , Chen, Bo-Hao , Li, Ying . Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data . | IEEE TRANSACTIONS ON MULTIMEDIA , 2018 , 20 (11) , 3045-3056 . |
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复杂环境声影响低信噪比动物声音的自动识别.为解决这一问题,本文提出一种不同声场景下低信噪比动物声音识别的方法.该方法把声音信号进行Bark尺度的小波包分解,再使用分解系数生成重构信号的频谱,并对频谱进行投影生成Bark频谱投影特征,通过随机森林分类器实现低信噪比动物声音的识别.该文分别在流水声环境、公路环境、风声环境和嘈杂说话声环境下,以不同的信噪比,对40种动物声音进行识别实验.结果表明,结合短时谱估计法、Bark频谱投影特征和随机森林的方法对不同信噪比的各种环境声音中动物声音的平均识别率可以达到80.5％,且在–10 dB的情况下依然保持平均60％以上的识别率.
Keyword ：
声音信号 声音信号 小波包变换 小波包变换 环境声音 环境声音 自动识别 自动识别 随机森林 随机森林
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| GB/T 7714 | 黄鸿铿 , 李应 . 用Bark频谱投影识别低信噪比动物声音 [J]. | 智能系统学报 , 2018 , 13 (4) : 610-618 . |
| MLA | 黄鸿铿 et al. "用Bark频谱投影识别低信噪比动物声音" . | 智能系统学报 13 . 4 (2018) : 610-618 . |
| APA | 黄鸿铿 , 李应 . 用Bark频谱投影识别低信噪比动物声音 . | 智能系统学报 , 2018 , 13 (4) , 610-618 . |
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As a technology of context analysis, the detective method of polyphonic sound event detection has a widespread prospect of application. In this paper, a detective method of polyphonic sound event were proposed to resolve the challenge in IEEE DCASE2017 task 3 based on full convolutional DenseNet. Relevant results illustrated that the method is higher in F-score and lower in ER than the baseline method proposed by IEEE DCASE2017 based on DNNs, and also get higher performance-7.4% higher in F-score and 3% lower in ER than the best method in IEEE DCASE2017 challenge based on CRNN. © 2018 IEEE.
Keyword ：
Audio recordings Audio recordings Big data Big data Blockchain Blockchain Cloud computing Cloud computing Convolution Convolution
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| GB/T 7714 | Zhe, He , Ying, Li . Fully Convolutional DenseNet based polyphonic sound event detection [C] . 2018 . |
| MLA | Zhe, He et al. "Fully Convolutional DenseNet based polyphonic sound event detection" . (2018) . |
| APA | Zhe, He , Ying, Li . Fully Convolutional DenseNet based polyphonic sound event detection . (2018) . |
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该文针对低信噪比噪声环境下的声音事件检测问题,提出基于多频带能量分布图离散余弦变换的声音事件检测的方法.首先,将声音数据转化为gammatone频谱,并计算其多频带能量分布;接着,对多频带能量分布图进行8×8分块与离散余弦变换;然后,对8×8的离散余弦变换系数进行Zigzag扫描,抽取离散余弦变换系数的主要系数作为声音事件的特征;最后,利用随机森林分类器对特征建模与检测.实验结果表明,在低信噪比及各种噪声环境下,该文提出的方法具有良好的检测效果.
Keyword ：
声音事件检测 声音事件检测 多频带能量分布 多频带能量分布 离散余弦变换 离散余弦变换 随机森林 随机森林
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| GB/T 7714 | 李应 , 吴灵菲 . 用多频带能量分布检测低信噪比声音事件 [J]. | 电子与信息学报 , 2018 , 40 (12) : 2905-2912 . |
| MLA | 李应 et al. "用多频带能量分布检测低信噪比声音事件" . | 电子与信息学报 40 . 12 (2018) : 2905-2912 . |
| APA | 李应 , 吴灵菲 . 用多频带能量分布检测低信噪比声音事件 . | 电子与信息学报 , 2018 , 40 (12) , 2905-2912 . |
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针对各种环境声对声音事件识别的影响,该文提出一种基于优化的正交匹配追踪(Orthogonal Matching Pursuit,OMP)声音事件识别方法。首先,利用OMP稀疏分解并重构声音信号,保留声音信号的主体部分,减小噪声的影响。其中,使用粒子群(Particle Swarm Optimization,PSO)算法优化搜索最优原子,实现OMP的快速稀疏分解。接着,对重构声音信号提取Mel频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCCs),与OMP时-频特征和基频(PITCH)特征,组成优化OMP的复合特征。最后,通过优化OMP复合特征,使用随...
Keyword ：
声音事件识别 声音事件识别 正交匹配追踪 正交匹配追踪 稀疏分解 稀疏分解 粒子群优化 粒子群优化 随机森林 随机森林
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| GB/T 7714 | 李应 , 陈秋菊 . 基于优化的正交匹配追踪声音事件识别 [J]. | 电子与信息学报 , 2017 , 39 (01) : 183-190 . |
| MLA | 李应 et al. "基于优化的正交匹配追踪声音事件识别" . | 电子与信息学报 39 . 01 (2017) : 183-190 . |
| APA | 李应 , 陈秋菊 . 基于优化的正交匹配追踪声音事件识别 . | 电子与信息学报 , 2017 , 39 (01) , 183-190 . |
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