Today I was working down at Sutton High School, teaching basic electronics to high school pupils. They learnt how to wire up an Arduinitar, a arduino based electric synthesis guitar with analog and digital control. This is part of Queen Marys outreach program.
More information on the Arduinitar is avaliable here http://www.eecs.qmul.ac.uk/~andrewm/arduinitar.html
I have recently completed a MOOC in data mining and machine learning, using Weka, a free open source machine learning toolkit.
For anyone interested in data mining, I strongly recommend this mooc and Weka https://weka.waikato.ac.nz/
It was announced today that my paper “Web Audio Evaluation Tool: A Browser-Based Listening Test Environment” has been accepted for the Sound and Music Computing Conference taking place in August.
This paper is a web audio API based development that provides users with a simple interface to construct and run perceptual audio evaluation experiments. Due to being browser based, there is no requirement for proprietary software, and it is easily extendable to any individual that can access a computer. It is set up so that no internet connection is required to run locally, or it can be hosted and post the XML output to a server.
This framework will give me an infrastructure with which to base all of me perceptual evaluation experiments which I will be undertaking in the next month or so.
Over the past few weeks, I have been working on evaluating a range of audio feature extraction tools. When I first started this project, I thought everyone uses the same features, so this should be easy – I was wrong.
I evaluated ten different feature extraction tools
- MIR Toolbox
- Timbre Toolbox
I discovered that from this set of toolboxes, there are only three features in common to all toolboxes –
spectral centroid spectral rolloff and signal energy.
In fact, of over 250 unique features, just 30 are present in more than half of the feature extraction tools.