Defense Date


Document Type


Degree Name

Master of Science



First Advisor

Afroditi Filippas


This study aimed to develop a real time multiple fundamental frequency detection algorithm for real time pitch to MIDI conversion applications. The algorithm described here uses neural network classifiers to make classifications in order to define a chord pattern (combination of multiple fundamental frequencies). The first classification uses a binary decision tree that determines the root note (first note) in a combination of notes; this is achieved through a neural network binary classifier. For each leaf of the binary tree, each classifier determines the frequency group of the root note (low or high frequency) until only two frequencies are left to choose from. The second classifier determines the amount of polyphony, or number of notes played. This classifier is designed in the same fashion as the first, using a binary tree made up of neural network classifiers. The third classifier classifies the chord pattern that has been played. The chord classifier is chosen based on the root note and amount of polyphony, the first two classifiers constrain the third classifier to chords containing only a specific root not and a set polyphony. This allows for the classifier to be more focused and of a higher accuracy. To further increase accuracy, an error correction scheme was devised based on repetitive coding, a technique that holds out multiple frames and compares them in order to detect and correct errors. Repetitive coding significantly increases the classifiers accuracy; it was found that holding out three frames was suitable for real-time operation in terms of throughput, though holding out more frames further increases accuracy it was not suitable real time operation. The algorithm was tested on a common embedded platform, which through benchmarking showed the algorithm was well suited for real time operation.


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Date of Submission

August 2012

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