  The fundamental frequency (F0) of human voice is generally controlled by
changing the vocal fold parameters (including tension, length and mass), which
in turn is manipulated by the muscle exciters, activated by the neural
synergies. In order to begin investigating the neuromuscular F0 control
pathway, we simulate a simple biomechanical arm prototype (instead of an
artificial vocal tract) that tends to control F0 of an artificial sound
synthesiser based on the elbow movements. The intended arm movements are
decoded from the EEG signal inputs (collected simultaneously with the kinematic
hand data of the participant) through a combined machine learning and
biomechanical modeling strategy. The machine learning model is employed to
identify the muscle activation of a single-muscle arm model in ArtiSynth (from
input brain signal), in order to match the actual kinematic (elbow joint angle)
data . The biomechanical model utilises this estimated muscle excitation to
produce corresponding changes in elbow angle, which is then linearly mapped to
F0 of a vocal sound synthesiser. We use the F0 value mapped from the actual
kinematic hand data (via same function) as the ground truth and compare the F0
estimated from brain signal. A detailed qualitative and quantitative
performance comparison shows that the proposed neuromuscular pathway can indeed
be utilised to accurately control the vocal fundamental frequency, thereby
demonstrating the success of our closed loop neuro-biomechanical control
scheme.
