This week I’ve been applying the final set of post-processing steps to the pitch and clap detection algorithms, and have been continuing to test both. Our pitch exercises, which are based on solfege, do not have jump in tones more than an octave. We leverage this fact to filter out unreasonable pitch measures, or pitch measures that are not the target of the exercise, like those generated from fricatives. I have also been working on extracting metrics from pitch exercises to evaluate a user’s performance. This task proved to be much more difficult to anticipate for many reasons, but most importantly, we were initially thinking about grading users with respect to some absolute pitch, instead of relative pitch. When implementing the evaluation metric, we figured it’d be too harsh of a constraint to have singers try to measure an exact pitch, so we have switched to judging their performance based on a translation of the original scale, which is defined by the users first note.
Next week, we will be integrating the detection algorithms with the front end, thus we will hopefully be able to get feedback from users for the functionality of our application which can drive a final set of changes if necessary.