摘要翻译：
啁啾现象是指信号的瞬时频率随时间变化的现象，广泛存在于生物系统的信号中。生物信号具有非平稳性，时频分析是分析生物信号的有效工具。众所周知，高斯chirplet函数是描述chirp信号的关键。尽管自适应chirplet变换(ACT)的理论已经建立了20多年，并在信号处理领域得到了广泛的认可，但ACT在生物/生物医学信号分析中的应用仍然相当有限，这可能是因为ACT作为一种新兴的生物信号分析工具，其威力尚未得到生物医学工程领域研究人员的充分认识。在本文中，我们描述了一种新的基于“粗-精”方案的ACT算法。即用匹配追踪(MP)算法实现chirplet的初始估计，然后用期望最大化(EM)算法对其进行细化，我们称之为MPEM算法。由于生物信号通常嵌入在强背景噪声中，因此增强算法对噪声的鲁棒性对生物信号分析具有重要意义。然后，我们将该算法应用于代表性生物信号的分析，包括视觉诱发电位（生物电信号）、可听心音和蝙蝠超声回声定位信号（生物声学信号）以及人类语音，从而证明了该算法的能力。结果表明，MPEM算法对所研究的信号有更紧凑的表示，对信号的时频结构有更清晰的可视化，表明ACT在生物信号分析中有很大的应用前景。MATLAB代码存储库托管在GitHub上供免费下载（https://GitHub.com/jiecui/mpact）。
---
英文标题：
《Biosignal Analysis with Matching-Pursuit Based Adaptive Chirplet
  Transform》
---
作者：
Jie Cui and Dinghui Wang
---
最新提交年份：
2017
---
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。
--

---
英文摘要：
  Chirping phenomena, in which the instantaneous frequencies of a signal change with time, are abundant in signals related to biological systems. Biosignals are non-stationary in nature and the time-frequency analysis is a viable tool to analyze them. It is well understood that Gaussian chirplet function is critical in describing chirp signals. Despite the theory of adaptive chirplet transform (ACT) has been established for more than two decades and is well accepted in the community of signal processing, application of ACT to bio-/biomedical signal analysis is still quite limited, probably because that the power of ACT, as an emerging tool for biosignal analysis, has not yet been fully appreciated by the researchers in the field of biomedical engineering. In this paper, we describe a novel ACT algorithm based on the "coarse-refinement" scheme. Namely, the initial estimate of a chirplet is implemented with the matching-pursuit (MP) algorithm and subsequently it is refined using the expectation-maximization (EM) algorithm, which we coin as MPEM algorithm. We emphasize the robustness enhancement of the algorithm in face of noise, which is important to biosignal analysis, as they are usually embedded in strong background noise. We then demonstrate the capability of our algorithm by applying it to the analysis of representative biosignals, including visual evoked potentials (bioelectrical signals), audible heart sounds and bat ultrasonic echolocation signals (bioacoustic signals), and human speech. The results show that the MPEM algorithm provides more compact representation of signals under investigation and clearer visualization of their time-frequency structures, indicating considerable promise of ACT in biosignal analysis. The MATLAB code repository is hosted on GitHub for free download (https://github.com/jiecui/mpact).
---
PDF链接：
https://arxiv.org/pdf/1709.08328