Detection of Musical Patterns in Real-Time

Following the publication of my paper, I was invited to collaborate on yet another research. This time involving the detection of musical patterns in real time. The project was the initial step in a larger idea of an Internet-of-Musical-Things system. The idea was this: “a smart musical instrument, which is able to convert musical notes to MIDI signals in real time, and a pattern detection system, which is able to recognize pre-programmed patterns, in real time, and use that data to control peripheral devices such as stage lights or smoke machines”.

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Cable car ride to 2200m!

I spent some time in the Tyrolian region of southern Austria in early 2016. It was mid January on a very late winter. I traveled around on my free time, and one weekend, I visited the breathtaking little town of Innsbrusk!

A little off the town center is the entry for the Nordkette Cable Car. Despite it being extremely cold, me and my friends decided to check out this experience, and Boy! we were stunned!

The cable car ran from Innsbruck to the top of the Hafelekarspitze mountain following the Hafelekar Run – which is supposed to be one of the steepest ski runs on Europe. There are two stops in the cable car ride, which ends at Hafelekar cirque – which is the top station of the Nordkette Cable Car. The views here are spectacular!

The top station of the Nordketten Cable Car at the Hafelekar cirque – Wikipedia
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Musical Onset Detection and the S-Transform

For a while throughout my last year of the masters program, I was thinking left right and center for a good project. I thought about several things, robotics, image processing, or an interesting gadget.

I landed on a brilliant idea! Why not do something about music, which I absolutely love. After racking my brain, I thought of designing an interesting and intelligent musical indexing and retrieval system – I will talk about this on a later post.

Upon doing some preliminary work, me and my supervisor realized that this is too much; there were so many things to consider – musical information retrieval, machine learning and sentiment analysis were some of the topics involved. The first stage of the work seemed sufficient in breadth and depth for a good research, so we stuck with it.

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