  Current computational-emotion research has focused on applying acoustic
properties to analyze how emotions are perceived mathematically or used in
natural language processing machine learning models. While recent interest has
focused on analyzing emotions from the spoken voice, little experimentation has
been performed to discover how emotions are recognized in the singing voice --
both in noiseless and noisy data (i.e., data that is either inaccurate,
difficult to interpret, has corrupted/distorted/nonsense information like
actual noise sounds in this case, or has a low ratio of usable/unusable
information). Not only does this ignore the challenges of training machine
learning models on more subjective data and testing them with much noisier
data, but there is also a clear disconnect in progress between advancing the
development of convolutional neural networks and the goal of emotionally
cognizant artificial intelligence. By training a new model to include this type
of information with a rich comprehension of psycho-acoustic properties, not
only can models be trained to recognize information within extremely noisy
data, but advancement can be made toward more complex biofeedback applications
-- including creating a model which could recognize emotions given any human
information (language, breath, voice, body, posture) and be used in any
performance medium (music, speech, acting) or psychological assistance for
patients with disorders such as BPD, alexithymia, autism, among others. This
paper seeks to reflect and expand upon the findings of related research and
present a stepping-stone toward this end goal.
