Imagine standing on a street corner in the city. With your eyes closed you can hear and recognize a succession of sounds: cars passing by, people speaking, their footsteps when they walk by, and the continuous falling of rain. The recognition of all these sounds and interpretation of the perceived scene as a city street soundscape comes naturally to humans. It is, however, the result of years of "training": 

Formulas for estimating and tracking the (time-dependent) frequency, form factor, and amplitude of harmonic time series are presented in this lecture note; in particular, sine-dominant signals, where the harmonics follow roughly the dominant first harmonic, such as photoplethysmography (PPG) and breathing signals. Special attention is paid to the convergence behavior of the algorithm for stationary signals and the dynamic behavior in case of a transition to another stationary state. The latter issue is considered to be important for assessing the tracking abilities for nonstationary signals.

Big data can be a blessing: with very large training data sets it becomes possible to perform complex learning tasks with unprecedented accuracy. Yet, this improved performance comes at the price of enormous computational challenges. Thus, one may wonder: Is it possible to leverage the information content of huge data sets while keeping computational resources under control? 

Smart home technologies, designed to make users happier, healthier, and wealthier, are rapidly becoming a mainstay of everyday life. In most cases, signal processing is essential to the devices' operation and performance. These days, a variety of intelligent automated devices can be found in nearly every home. The trend is accelerating so rapidly that it now appears inevitable that smart technology will soon be integrated into virtually every facet of daily life.

It’s been a while since I last wrote a column for IEEE Signal Processing Magazine. I will try to address here some of the many questions and changes that arose since the beginning of the year. But before I do so, I would like to invite you to watch a short documentary by Ben Proudfoot with the exact title of this column: “She Changed Astronomy Forever. He Won the Nobel Prize for It.”