DSP Basics

capturing the speech input for human–computer conversations [Cassell et al. 2000], hand gestures to input text [Jones et al. 2010], eye gaze to facilitate more efficient interaction [Pfeuffer et al. 2015], recording biosignals such as heart rate to understand the body [Schmidt 2015], and adapting digital content based on envi­ ronmental conditions or locations [Rodden et al. 1998], in each of the case studies described later in this book. All must capture and then process sensor data in order to provide input or adapt output in a digital system. In many cases, the interactive systems developer must convert, filter, transform, and/or threshold raw signal data (from e.g., an accelerometer, a microphone, or an electrocardiogram [ECG] sensor) into a form suitable for use in their application. The choices made in each DSP step have implications on the quality of the resulting output used to direct the decisions made by their application. To get the reader started, we aim to provide a beginner’s guide to DSP. How­ ever, this introduction is far from exhaustive. Indeed, there are numerous great textbooks dedicated to the advanced understanding of this topic and the reader is encouraged to consult them for more in-depth theoretical knowledge. Recom­ mended reading includes the “Digital Signal Processing” titles by Proakis and Manolakis [2007] and Rawat [2015]. DSP basics begins with an introduction to the different types of signals and continues to cover the analog-to-digital conversion topics of sampling, quantization, and coding. From there it covers digital-to-analog conversion, discrete Fourier transforms (DFTs), autocorrelation, and linear time-invariant DSP Basics