This study presents a novel approach to assess the perception of auditory Absolute threshold (ATTh) in healthy individuals exposed to noise and solvents in their occupational environment using machine learning approaches. 396 subjects with no known history of auditory pathology were chosen from three groups, namely, employees from Chemical Industries (CI), Fabrication Industries (FI), and professional Basketball Players (BP), with each category having 132 subjects. Absolute Threshold Test (ATT) was developed using MATLAB and the experiment was conducted in a silent, noise-free environment. ATTh was obtained twice, during the commencement and conclusion of the employees' workshift in CI and FI. For BP, ATTh was obtained before and after their basketball training sessions and was used as features for binary SVM classification approach, in which the RBF kernel-based technique was found to provide maximum accuracy as compared to linear and quadratic approach. For three-class classification, MLP neural network with Levenberg-Marquardt training function in the hidden layer and Mean Square Error function in the output layer was found to be optimal along with k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF), in which, an accuracy of 81.06% was observed in kNN approach and 92.4% using MLP neural network approach, whereas SVM yielded an accuracy of 93.94% in the classification of the subjects into CI, FI and BP, showing that the SVM outperformed kNN and MLP neural network for healthy subjects based on their occupational exposure/professional sports training. Such machine learning approaches could further be probed into, to improve the accuracy of classification. Also, such techniques can help in real-time classification of subjects based on their occupational exposure so as to predict and prevent plausible permanent hearing dysfunction due to occupational exposure as well as to aid in sports rehabilitation and training programs to assess the auditory perceptive abilities of the individuals.

Psychoacoustics is considered to be a category of psychophysics and is termed as the assessment of auditory or sound perception and deals with perception of sound among different human beings. Psychoacoustics is a very qualitative phenomenon and is often the reason for an individual to perceive the sound in different manner, as compared to another. This effect is more prominent in music wherein a given music bit may seem good to one, whereas, another may perceive it as bad. For this to happen, various attributes of sound play an important part, in the categorization based on psychoacoustical perception. The present work considered pitch as the varying attribute and provided a comparative assessment of sound, based on the variation of pitch in terms of Pitch Detection Threshold (PDTh) between trained musicians and non-musicians. 44 subjects of both conditioned and controlled set were considered for this study and were made to undergo the Pitch Detection Test (PDT) to assess their PDTh. Two trials were conducted, once in the morning and the other in the evening. The results provided substantial cue to conclude that the musicians had a better PDTh (6.83 dB) than the control set (31.31 dB). Also, the PDTh was better in the morning trials, as compared to their evening counterparts. This could be attributed to the professional training in music, due to which the conditioned set outperformed the control set of subjects. Such an analysis could aid in the assessment of auditory perceptive abilities and their improvement with music and hence indicate the plausible improvement in the auditory perception with music based raaga therapy. Further, more attributes such as intensity and frequency could be encompassed to provide stronger relationship between perception and learning, in this case being music training sessions, which could also work as therapy for certain auditory perceptive disorders.

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