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Current computational-emotion research has focused on applying acousticproperties to analyze how emotions are perceived mathematically or used innatural language processing machine learning models. While recent interest hasfocused on analyzing emotions from the spoken voice, little experimentation hasbeen 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 likeactual noise sounds in this case, or has a low ratio of usable/unusableinformation). Not only does this ignore the challenges of training machinelearning models on more subjective data and testing them with much noisierdata, but there is also a clear disconnect in progress between advancing thedevelopment of convolutional neural networks and the goal of emotionallycognizant artificial intelligence. By training a new model to include this typeof information with a rich comprehension of psycho-acoustic properties, notonly can models be trained to recognize information within extremely noisydata, but advancement can be made toward more complex biofeedback applications-- including creating a model which could recognize emotions given any humaninformation (language, breath, voice, body, posture) and be used in anyperformance medium (music, speech, acting) or psychological assistance forpatients with disorders such as BPD, alexithymia, autism, among others. Thispaper seeks to reflect and expand upon the findings of related research andpresent a stepping-stone toward this end goal.

Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers