[电气工程与系统科学] 基于局部二值模式的肺音分类 [推广有奖]

摘要翻译：
肺音包含有关肺部病理的重要信息。本文利用肺音的短时频谱特征来识别相关疾病。基于听觉感知技术在语音信号分类中的成功应用，我们使用Mel尺度扭曲谱系数来表示肺音的时频信息，这里称为Mel频谱系数(MFSCs)。然后，利用局部二值模式分析(LBP)来获取MFSCs的纹理信息，并利用直方图表示法导出特征向量。将所提出的特征用于该领域的三个著名分类器：k-最近邻(kNN)、人工神经网络(ANN)和支持向量机(SVM)。同时，用多个支持向量机核对其性能进行了测试。我们使用两个数据库进行了广泛的性能评估实验，其中包括正常声音和不定声音。结果表明，基于支持向量机和kNN分类器的特征在异常音检测方面优于常用的基于小波的特征和基于MFCCs的统计特征。所提出的特征也比由有理膨胀小波系数计算的形态特征和能量特征具有更好的效果。Bhattacharyya内核的性能比其他内核好得多。进一步，我们优化了所提出的特征提取算法的配置。最后，采用基于最小冗余度最大关联度的特征选择方法去除特征向量中的冗余，在不降低性能的前提下提高了算法的计算效率。与标准的基于小波特征的系统相比，总体性能提高了24.5%。
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英文标题：
《Lung sound classification using local binary pattern》
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作者：
Nandini Sengupta, Md Sahidullah, Goutam Saha
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Lung sounds contain vital information about pulmonary pathology. In this paper, we use short-term spectral characteristics of lung sounds to recognize associated diseases. Motivated by the success of auditory perception based techniques in speech signal classification, we represent time-frequency information of lung sounds using mel-scale warped spectral coefficients, called here as mel-frequency spectral coefficients (MFSCs). Next, we employ local binary pattern analysis (LBP) to capture texture information of the MFSCs, and the feature vectors are subsequently derived using histogram representation. The proposed features are used with three well-known classifiers in this field: k-nearest neighbor (kNN), artificial neural network (ANN), and support vector machine (SVM). Also, the performance was tested with multiple SVM kernels. We conduct extensive performance evaluation experiments using two databases which include normal and adventitious sounds. Results show that the proposed features with SVM and also with kNN classifier outperform commonly used wavelet-based features as well as our previously investigated mel-frequency cepstral coefficients (MFCCs) based statistical features, specifically in abnormal sound detection. Proposed features also yield better results than morphological features and energy features computed from rational dilation wavelet coefficients. The Bhattacharyya kernel performs considerably better than other kernels. Further, we optimize the configuration of the proposed feature extraction algorithm. Finally, we have applied mRMR (minimum-redundancy maximum-relevancy) based feature selection method to remove redundancy in the feature vector which makes the proposed method computationally more efficient without any degradation in the performance. The overall performance gain is up to 24.5% as compared to the standard wavelet feature based system.
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PDF链接：
https://arxiv.org/pdf/1710.01703