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Introduction The process by which successive sounds from one source (such as a violin or a person talking) are perceptually grouped together and separated from other competing sounds is known as auditory stream segregation, or simply “auditory streaming” (Bregman, 1994). One of the major cues that help in auditory stream segregation is spectral profiling. Spectral profile analysis refers to identifying the change in spectral profile when intensity of one of the components of complex tone is altered in intensity. This is important for auditory stream segregation as the spectra of sound sources are characterized by their pattern of intensity variation as a function of frequency. Need for the study Musicians are a group of individuals who are trained to perceive fine structural variation in the acoustic stimuli. It is well reported in the literature that musical training enhances the ability of coding fast varying auditory signal (Strait, O’connell, Parbery-Clark, & Kraus, 2013). Studies have also shown that musicians have enhanced temporal perception (Rammsayer & Altenmüller, 2006), speech perception in noise (Jain, Mohamed, & Kumar, 2015) and better fine structure abilities (Mishra, Panda, & Raj, 2015). The role of musical training has also been studied extensively in the context of auditory skills, including auditory streaming (Zendel & Alain, 2009). There are no studies reported in the literature which have attempted to understand the effect of auditory streaming abilities in musicians. Thus, the aim of the present was to analyze the differences in spectral profile thresholds in individuals with and without musical training. Method A standard group comparison was adapted by considering 60 individuals with normal hearing within the age range of 15-30 yrs. The participants were divided into 2 groups: Group 1 consisted of 30 musicians and group 2 consisted of 30 non-musicians. The test in the MATLAB software with psychoacosutics toolbox was used to assess the individual’s sensitivity to auditory stream segregation through profile analysis task. The stimuli had five harmonics all at the same amplitude (f0=330-Hz, mi4). The third (variable tone) has a similar harmonic structure; however, the amplitude of the third harmonic component is higher producing a different timbre in comparison to the standards. The subject had to identify the odd timbre tone. The testing was carried out at 60 dB HL in a sound treated room. Results and Discussion Shapiro Wilk test of normality suggested that the data was normally distributed (p>0.05). Independent t-test was carried out to compare profile analysis threshold between the two groups. The results of the study showed that the thresholds were significantly better (p<0.05) in musicians compared to non-musicians. The effect size was calculated using the formula r=Z/√N. The effect size was found to be 0.78 which shows that the significance is strong. In addition, Pearson’s product moment correlation was done to determine if there is any correlation between profile analysis threshold number of years of music training. The result of the correlation analysis shows that the thresholds were better with increase in duration of music training. The results of the present study are in consensus with previous studies which report that musicians have better auditory stream segregation skills (Marozeau et al., 2010; Zendel & Alain, 2009). Previous studies have shown that As a result of training, musicians are more sensitive to changes in auditory stimuli based on pitch, time and loudness (Marozeau, Innes-Brown, & Blamey, 2013; Marozeau, Innes-Brown, Grayden, Burkitt, & Blamey, 2010), with discrimination thresholds being lower in musicians than in non-musicians. Thus, improved auditory processing in musicians could have resulted in better profile analysis threshold. Conclusions The present study attempted to determine if there are any differences in auditory stream segregation through profile analysis task between musicians and non-musicians. The results of the study showed that spectral profile analysis abilities were better in musicians compared to non-musicians. In addition, it was also found that the number of years of music training correlated with profile analysis thresholds. Thus, auditory stream segregation was found to be better in musicians compared to non-musicians and the performance improved with increase in number of years of training. However, further studies are essential on a larger group with more variables for validation of the results.
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