Two of us spent more time on their algorithm to seperate single keys from a signal with multiple keys in it. For this, they each worked on a different approach of the problem. One algorithm is almost finished and is able to find all the keypresses in an entire signal with little noise. However when testing it with a signal that consists of only background noise, it had 10 false-positives. This algorithm seems to require a relatively clean sample.

The other one approached the problem by 'reducing' the signal. This means selectively deleting data, so the overall shape and size is still intact, but the almost random oscillations are removed. This makes the signal more manageable, and makes it easier to identify different peaks, thus making it easier to find a pattern to eventually detect a keysound.

We also explored other ways to analyse the keys (such as Cepstrum coefficient in the quefrency domain, we were also interested in the wavelets analysis(not yet fully explored). We looked up more information on internet and in the matlab help.

The other part of our team continued working on the problems they faced the last session (see previous blog). After rereading some articles, we found a way to make the peaks more clear using energyvectors(=deltavector). In the timedomain we seperated different windows of width 2ms, transformed these into the frequency-domain(using fft) and summated the entire vector. By summating these values ans substracting each vector with the last, we create a delta vector. To create a more clear signal, we substracted each element of the signal with the next one, this cancells out alot of random noise, and also makes the signal much clearer. This results in a much better precision for recognizing keysounds.