Worked on the issues mentioned during interim demo, which were not being able to accurately detect slurs and not picking up the rests in songs.
First, experimented with modifying the code to Short Time Energy (STE). This helped the code become more “clear” as there were less bumps and more clear near zero values, essentially eliminating some of the noise that stayed with RMS. Should make amplifying the signal a lot easier now. However, still having some difficulty seeing the differences in slurred notes, so doing some additional research in onset detection to detect slurred notes specifically rather than look for the separation of notes in segmentation.
(forgot to change label for the line, but this is a graph for STE – this was taken using audio from a student in the school of music)
For rests, modified my rhythm detection algorithm to instead look for zero values after reaching the peak (means the note is done playing) and taking the additional length after the note to count as a rest. Sometimes takes slight moments of silence to distinguish notes as a rest though so need to do some experiments to make it less sensitive.