Wednesday, 26 January 2022

The 30th Annual Running Of The University Of Oxford Digital Signal Processing Course Will Be Held Online Again, In 2022

The 30th annual running of the University Of Oxford Digital Signal Processing course will be running over a six week period, from Monday 14 Feb 2022 - Friday 25 Mar 2022.

The course first moved online in 2020 and has received excellent reviews from the attendees.

Based on the classroom course, Digital Signal Processing (Theory and Application), this online course consists of weekly live online tutorials and also includes a software lab that can be run remotely. We'll include all the same material, many of the existing labs and all the interaction of the regular course.

Online tutorials are delivered via Microsoft Teams once each week and practical exercises are set to allow you to practice the theory during the week. 

You will also have access to the course VLE (virtual learning environment) to communicate with other students, view and download course materials and tutor support is available throughout.

Code examples will be provided although no specific coding experience is required. 

The live tutorials will be on Wednesday each week from 13:00 - 14:30 and 15:00 - 16:30 (GMT) with a 30-minute break in between.

You should allow for 10 - 15 hours study time per week in addition to the weekly lessons and tutorials.

After completing the course, you should be able to understand the workings of the algorithms we explore in the course and how they can solve specific signal processing problems.

Full details are available here:

Sunday, 23 January 2022

Analog I/O Example For The NXP LPC55S69-EVK

 I recently wrote some DSP code for the excellent NXP LPC55S69-EVK.

The standard audio I/O example for the LPC55S69-EVK implements a very simple piece of code to read in an array of audio data from the stereo codec and then write it back. It does not show how to access that data or process it using background DSP functions.

I have written an example that uses interrupts, ping-pong buffers and background tasks to apply DSP functions to the real-time audio datastream.

The example can be downloaded from:

Version 10.00 Of The SigLib DSP Library Released And Is Now Fully Open Source

SigLib V10 now includes enhanced functions for training and inferring Artificial Intelligence and Machine Learning Convolutional Neural Networks (CNNs). In addition to the traditional DSP functions, the SigLib ML functions are designed for embedded applications such as vibration monitoring etc. They are architected for Edge-AI applications and have been written for the highest level of MIPS and memory optimization.

Containing over 1000 DSP and ML functions, SigLib is now available with a dual open source (GPL) and commercial license and is available from GitHub at: