Non-stationary Signal Feature Extraction and Analysis

非平稳信号特征提取与分析

• 批准号: RGPIN-2015-03990
• 负责人: Krishnan, Sridhar
• 金额: $ 2.7万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656017
• 关键词: Non; stationary; Signal; Feature; Extraction

Signal processing continues to play a fundamental role in many technological innovations and advancements related to speech, multimedia, healthcare, defense, security, telecommunications, Internet,  and energy systems. For the past 15 years, the Signal Analysis Research (SAR) Group at Ryerson University is involved in developing various innovative techniques and algorithms for processing and analysis of speech, audio, multimedia and biomedical signals. The underlying characteristics of signals involved with these systems is that they are complex, typically long duration, difficult to interpret, and have time-varying properties. In order to extract valuable information (features) from these signals and characterize events of interest, and to automatically classify patterns, sophisticated signal analysis algorithms (and analytical tools) need to be designed. The proposed NSERC Discovery Grant research will systematically investigate and develop mathematical methods, algorithms and tools to map 1-dimensional (1D) signals into higher dimensions for automatically extracting signal features at multiple levels, which are otherwise difficult or impossible to extract from conventional techniques. It is envisioned the mathematical transformation of signals to higher dimensions and the subsequent feature extraction algorithms will reveal underlying signal generation/modification mechanisms that could be useful in recognizing hidden/subtle signatures for better recognition and classification applications. The extracted signal features will be further coupled with appropriate machine learning algorithms in providing enhanced and robust recognition and classification performance efficiencies. Automatic feature extraction and analysis has lots of practical applications, and is the foundation of everyday systems encountered in speech, audio, multimedia,  biometrics and many other intelligent systems. The algorithms will be applied to real world datasets collected in our lab and other open source databases. The algorithms and the databases will also be shared with other interested research groups for the benefit of their specific domain of application (e.g., big data analytics in energy or health sector). The research program will also train a large number of highly qualified personnel who could eventually lead technological advancement in various industry and research sectors that are crucial for the societal well-being and economic prosperity of Canada.**

过去 15 年来，瑞尔森大学的信号分析研究 (SAR) 小组一直在与语音、多媒体、医疗保健、国防、安全、电信、互联网和能源系统相关的许多技术创新和进步中发挥着重要作用。大学涉及用于处理和分析语音、音频、多媒体和生物医学信号的各种创新技术和算法，这些系统涉及的信号的基本特征是它们很复杂，通常持续时间长，难以解释，并且有时间 -变化的为了从这些信号中提取有价值的信息（特征）并描述感兴趣的事件，并自动对模式进行分类，需要设计复杂的信号分析算法（和分析工具），最终需要设计 NSERC 发现资助研究。研究开发数学方法、算法和工具，将一维 (1D) 信号映射到更高维度，以自动提取多个级别的信号特征，否则很难或不可能从传统技术中提取信号特征。更高的维度和后续的特征提取算法将揭示潜在的信号生成/修改机制可用于识别隐藏/微妙的签名，以实现更好的识别和分类应用。提取的信号特征将进一步与适当的机器学习算法相结合，以提供增强且强大的识别和分类性能效率。和分析有很多实际应用，是语音、音频、多媒体、生物识别和许多其他智能系统中遇到的日常系统的基础，这些算法将应用于我们实验室收集的现实世界数据集和其他开源算法。和数据库还将与其他感兴趣的研究小组共享，以促进其特定应用领域（例如能源或健康领域的大数据分析）。该研究计划还将培训大量高素质人才，他们最终可能引领技术进步。对加拿大的社会福祉和经济繁荣至关重要的各个行业和研究领域。**