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Urban soundscape prediction based on acoustic ecology and MFCC parameters
Noviyanti A.a, Sudarsono A.S.b, Kusumaningrum D.a
a Product Design Engineering, Universitas Prasetiya Mulya, Tangerang, Indonesia
b Engineering Physics, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 Author(s).Many studies have been conducted to predict urban soundscape based on acoustic parameters. In this study, the prediction of urban soundscape composition based on acoustic ecology and MFCC parameters is conducted using binary logistic regression. Six parameters of acoustic ecology (ACI, ADI, AEI, BI, H, and NDSI) and 12 MFCC parameters were used to predict the perception of relaxation, dynamic and communication. A dataset of 600 urban sonic environment compositions with the perception ratings (based on the perception of relaxation, dynamic, and communication) was used in this study. The acoustic ecology and MFCC parameters were calculated from the sonic environment composition audio files. The analysis using binary logistic regression shows that parameters of MFCC give significant level at 90 % for the perception of relaxation, dynamic, and communication. The model prediction based on the significant parameter gives the Correct Classification Rate: relaxation (CCR = 88.3 %), dynamic (CCR = 77.6 %), and communication (CCR = 59.3 %). The results indicate that the parameter of MFCC could be a better predictor of sound perception rather than the acoustic ecology.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1063/1.5138335[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]