TY - GEN
T1 - Stronger correlation of music features with brain signals predicts increased levels of enjoyment
AU - Pandey, Pankaj
AU - Bedmutha, Poorva Satish
AU - Miyapuram, Krishna Prasad
AU - Lomas, Derek
PY - 2023
Y1 - 2023
N2 - Music recommendation systems struggle with predicting the aesthetic responses of listeners based solely on acoustic characteristics, which are dependent on the listener's perception. This research correlates acoustic music features with brain responses to report the neural aesthetic hypothesis that the intensity of an aesthetic experience can be decoded based on the degree of correlation to brain responses. We employ hybrid encoding-decoding model (Canonical Correlation Analysis) to identify music features that maximally covary with brain responses. EEG signals of 20 participants are analyzed while they listen to 12 songs and mark their enjoyment on a scale of 1 to 5. Firstly, 18 acoustic features are extracted from music signals and transformed into the first principal component (PC1). In addition, two other features used for analysis are root mean square (RMS) and Spectral Flux (Flux). The first principal canonical component (CC1) with PC1 determines significant (p<0.05) evidence of correlating with brain responses that increasing correlation reflects increased enjoyment. We consider each participant's average CC1 values and enjoyment rating over all 12 songs, followed by plotting a correlation graph to decode the relationship. We observe a significant (p<0.05) positive linear correlation with increasing CC1 scores of PC1 features against increased enjoyment rating. PC1 shows the maximum Pearson correlation (r = 0.48, p = 0.03). In addition, we segregate the brain responses based on low (1,2) and high (3,4) enjoyment ratings and find that higher CC1 values correspond to brain responses of high enjoyment and low values to low enjoyment in all three features. Our experiments reveal that Canonical correlation reflects music-induced pleasure and can be employed in EEG-enabled headphones to decode the user experience, leading to better recommendations.
AB - Music recommendation systems struggle with predicting the aesthetic responses of listeners based solely on acoustic characteristics, which are dependent on the listener's perception. This research correlates acoustic music features with brain responses to report the neural aesthetic hypothesis that the intensity of an aesthetic experience can be decoded based on the degree of correlation to brain responses. We employ hybrid encoding-decoding model (Canonical Correlation Analysis) to identify music features that maximally covary with brain responses. EEG signals of 20 participants are analyzed while they listen to 12 songs and mark their enjoyment on a scale of 1 to 5. Firstly, 18 acoustic features are extracted from music signals and transformed into the first principal component (PC1). In addition, two other features used for analysis are root mean square (RMS) and Spectral Flux (Flux). The first principal canonical component (CC1) with PC1 determines significant (p<0.05) evidence of correlating with brain responses that increasing correlation reflects increased enjoyment. We consider each participant's average CC1 values and enjoyment rating over all 12 songs, followed by plotting a correlation graph to decode the relationship. We observe a significant (p<0.05) positive linear correlation with increasing CC1 scores of PC1 features against increased enjoyment rating. PC1 shows the maximum Pearson correlation (r = 0.48, p = 0.03). In addition, we segregate the brain responses based on low (1,2) and high (3,4) enjoyment ratings and find that higher CC1 values correspond to brain responses of high enjoyment and low values to low enjoyment in all three features. Our experiments reveal that Canonical correlation reflects music-induced pleasure and can be employed in EEG-enabled headphones to decode the user experience, leading to better recommendations.
KW - Aesthetic
KW - CCA
KW - EEG
KW - Music
UR - http://www.scopus.com/inward/record.url?scp=85158048137&partnerID=8YFLogxK
U2 - 10.1109/APSCON56343.2023.10101229
DO - 10.1109/APSCON56343.2023.10101229
M3 - Conference contribution
AN - SCOPUS:85158048137
T3 - APSCON 2023 - IEEE Applied Sensing Conference, Symposium Proceedings
BT - APSCON 2023 - IEEE Applied Sensing Conference, Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2023 IEEE Applied Sensing Conference, APSCON 2023
Y2 - 23 January 2023 through 25 January 2023
ER -