Non-stationary Signal Feature Extraction and Analysis

信号处理在与语音、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继续发挥着重要作用。在过去的15年里，瑞尔森大学的信号分析研究（SAR）小组参与开发各种创新技术和算法，用于处理和分析语音，音频，多媒体和生物医学信号。与这些系统相关的信号的基本特征是它们是复杂的，通常持续时间长，难以解释，并且具有时变特性。为了从这些信号中提取有价值的信息（特征）并表征感兴趣的事件，以及自动分类模式，需要设计复杂的信号分析算法（和分析工具）。拟议的NSERC发现资助研究将系统地调查和开发数学方法，算法和工具，将一维（1D）信号映射到更高的维度，以自动提取多个级别的信号特征，这些特征难以或不可能从传统技术中提取。可以预见的是，信号到更高维度的数学变换和随后的特征提取算法将揭示潜在的信号生成/修改机制，这可能有助于识别隐藏/微妙的签名，以获得更好的识别和分类应用。所提取的信号特征将进一步与适当的机器学习算法相结合，以提供增强的和鲁棒的识别和分类性能效率。自动特征提取和分析具有广泛的实际应用，是语音、音频、多媒体、生物识别等智能系统的基础。这些算法将应用于我们实验室和其他开源数据库中收集的真实的世界数据集。算法和数据库也将与其他感兴趣的研究小组共享，以利于其特定的应用领域（例如，能源或卫生部门的大数据分析）。该研究计划还将培养大量高素质的人才，他们最终可以领导对加拿大社会福祉和经济繁荣至关重要的各个行业和研究部门的技术进步。

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.**