Signal Processing for Machine Learning

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• published: 06 Mar 2015
• views: 11820

Signals are ubiquitous across many research and development domains. Engineers and scientists need to process, analyze, and extract information from time-domain data as part of their day-to-day responsibilities. In a range of predictive analytics applications, signals are the raw data that machine learning systems must be able to leverage for the purpose of creating understanding and for informing decision-making. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We use consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and we finally train a small neural network to map the feature vectors into the six different activity classes of a prerecorded dataset. We show how the joint use of MATLAB(R) and library functions help deliver high-performance results with few design iterations and concise, clear code. The topics discussed include: Signal manipulation and visualization Design and application of digital filters Frequency-domain analysis Automatic peak detection Feature extraction from signals Train and test of simple neural networks