Low-cost didactic platform for real-time adaptive filtering: Application on noise cancellation

Abstract

Didactic platforms, used in real-time digital signal processing courses, are generally dedicated digital signal processors or field-programmable gate arrays. These devices are expensive and difficult to program, preventing their widespread use in signal processing courses. On the other hand, the new technology of digital signal controllers, microcontrollers with floating point and mathematical operations, can reduce the cost of dedicated platforms for real-time digital signal processors, facilitating the development of digital signal processors projects and educational applications, such as teaching adaptive filters. Here, we present a low-cost didactic platform for developing real-time adaptive filters using the digital signal processors hardware based on the ARM Cortex-M7 processor. We present the theoretical aspects of the least mean squares and normalized least mean squares algorithms and an experimental script to help students learn real-time adaptive filters. We also describe the platform structure and the performance measurement, in terms of mean square error, signal-to-noise ratio, and computational efficiency. Finally, we present a brief discussion on the use of this platform in classes and the improvement in the student engagement and attendance.