For this project, I used spectral analysis along with machine learning to create a system for chord recognition. The system works by writing FFT frequency bin amplitudes into a matrix, then taking “snapshots” of the matrix and outputting the snapshot as a list, then sending these lists to the ml.svm object for categorization. While the system could easily work with any audio source, for this demonstration I made a simple polyphonic synth using sawtooth oscillators and a MIDI controller to play chords for the system to analyze. The challenge with this project was devising a system for processing the data from the FFT matrix and molding it into a form that is usable by the SVM but still contains enough data to identify specific chord spectra. The algorithm is powerful enough to recognize, for example, the difference between a C major chord and a C minor chord, if given enough training data.
In this demonstration I show how to train the SVM and how to map new chords. At the end I show that the system is not aware that a chord played an octave higher is not recognized. This can be fixed easily by simply mapping one chord played in several octaves (for example play C major chords with roots C3, C4, and C5 as state 1, D3 D4 D5 state 2, etc.)
