Abstract
Assessing blending of instruments is important in music performance and perception research, but remains underexplored due to its complex multi-dimensional nature. Despite extensive research on source-level blending, the influence of room acoustics on this process is rarely examined. This study proposes a computational modelling approach to evaluate the perceived overall blending between instruments examining the blending at the source-level and its alteration brought by room acoustics. Three audio stimuli, each showcasing different degrees of source-level blending between two violins, were auralized in 25 simulated room acoustic environments, with expert listeners assessing their overall perceived blending. The correlation analysis of room acoustic parameters revealed that their influence on overall blending is contingent upon source-level blending. A random forest regression model is proposed to predict perceived overall blending ratings using source-level blending ratings and room acoustic parameters. Its viability was confirmed through twofold evaluation, including Leave-one-out-cross-validation and separate training and test data, with a mean absolute error of 6% in each case. Feature importance analysis revealed that source-level blending contributes 60%, while room acoustics contribute 40% of the overall perceived blending ratings, with perceived reverberance being the primary contributor. Overall, this investigation contributes to a more holistic understanding of blending perception.