Frequency-based interpolation of sampled signals with applications in audio restoration

A new interpolation technique has been developed for replacing missing samples in a sampled waveform drawn from a stationary stochastic process, given the power spectrum for the process. The method works with a finite block of data and is based on the assumption that components of the block DFT are Gaussian zero-mean independent random variables with variance proportional to the power spectrum at each frequency value. These assumptions make the interpolator particularly suitable for signals with a sharply-defined harmonic structure, such as audio waveforms recorded from music or voiced speech. Some results are presented and comparisons are made with existing techniques.

Towards real-time profiling of sprints using wearable pressure sensors

On-body sensor systems for sport are challenging since the sensors must be lightweight and small to avoid discomfort, and yet robust and highly accurate to withstand and capture the fast movements associated with sport. In this work, we detail our experience of building such an on-body system for track athletes. The paper describes the design, implementation and deployment of an on-body sensor system for sprint training sessions. We autonomously profile sprints to derive quantitative metrics to improve training sessions. Inexpensive Force Sensitive Resistors (FSRs) are used to capture foot events that are subsequently analysed and presented back to the coach. We show how to identify periods of sprinting from the FSR data and how to compute metrics such as ground contact time. We evaluate our system using force plates and show that millisecond-level accuracy is achievable when estimating contact times. © 2012 Elsevier B.V. All rights reserved.