For amateur musicians learning to improvise can be a demanding task. It can take up a lot of time and energy. The goal of this project is to simplify the learning process for jazz style pianist. This will be done by showing the musician notes that could be played in a clear manner and generating new notes when the user deviates from the proposed notes. To accomplish this goal the project is divided into 3 parts: A hardware component that will focus on displaying the notes and handling inputs, a signal processing part that focuses on extracting the frequencies from an audio stream in real time to determine what notes are being played. A machine learning part that will develop a model based on jazz solos and is able to use given chord progressions to generate jazz melodies.

This thesis is focused on the signal processing subgroup and their task to create the algorithm that will determine what notes are played and when they are played. To accomplish this task, the signal from the Hardware group is put through pre-processing consisting of a bandpass filter. Then using spectral flux to compute onset detection to determine when a new note is played. Followed by a harmonic product spectrum algorithm to determine the frequency that is played and finally outputting the corresponding MIDI note to the Machine Learning subgroup. Various tests were done on a real piano and the results were promising, with low latency, reliable note detection and a high accuracy. ... Read more