This document describes using Hidden Markov Models to determine hidden chord parameters from observable melody bars of music. It calculates transition and observation probabilities from expert knowledge on music and 250 lead sheets. The Viterbi algorithm is then used to find the most likely chord sequence given the melody notes by maximizing the probability of chords given the melody notes. Target users include general and intermediate users who would get chord analysis, and technical experts and professionals who could also evaluate the performance and technology.

10. Bar (Static
measure)

11. HMM can modeled to determine hidden
parameters from the observable data.

14. Observations(Melody bars)
Observation
Probability
Transition
Probability Hidden States (Chords )

15. 250 Lead Sheets
Expert Knowledge on Music
Calculate
Aij=P (Chord i / Chord i-1) B – Observation Probability matrix
A – Transition Probability
Ai,j = αAmaji,j X (1-α) Amini,j Matrix
α- Emotional Factor
Bij=P (MelodyBar / Chord) . Di,j D – Pitch Class Vector
π = P(StartChord/Chord)

16. Given A , B , π
Max P(Chords/MelodyNotes ) = Viterbi Path=?

17. Technolog
Targeted group Concept End product Performance
y
General Users (Novice) (30) 100% 90% - -
Intermediate-Musicians(15) 100% 80% - -
Technical Expert (3) 95% - 75% 75%
Professional Musicians(6) 100% 80% 80% -