Cross-modal association analysis and matching model construction of perceptual attributes of multiple colors and combined tones - Wang, Shuang; Liu, Jingyu; Lan, Xuedan; Hu, Qihang; Jiang, Jian; Zhang, Jingjing (Frontiers in Psychology, 2022)
Audio-visual correlation is a common phenomenon in real life. In this article, aiming at analyzing the correlation between multiple colors and combined tones, we comprehensively used experimental methods and technologies such as experimental psychology methods, audio-visual information processing technology, and machine learning algorithms to study the correlation mechanism between the multi-color perceptual attributes and the interval consonance attribute of musical sounds, so as to construct an audio-visual cross-modal matching models. Specifically, in the first, this article constructed the multi-color perceptual attribute dataset through the subjective evaluation experiment, namely “cold/warm,” “soft/hard,” “transparent/turbid,” “far/near,” “weak/strong,” pleasure, arousal, and dominance; and constructed the interval consonance attribute dataset based on calculating the audio objective parameters. Secondly, a subjective evaluation experiment of cross-modal matching was designed and carried out for analyzing the audio-visual correlation, so as to obtain the cross-modal matched and mismatched data between the audio-visual perceptual attributes. On this basis, through visual processing and correlation analysis of the matched and mismatched data, this article proved that there is a certain correlation between multicolor and combined tones from the perspective of perceptual attributes. Finally, this article used linear and non-linear machine learning algorithms to construct audio-visual cross-modal matching models, so as to realize the mutual prediction between the audio-visual perceptual attributes, and the highest prediction accuracy is up to 79.1%. The contributions of our research are: (1) The cross-modal matched and mismatched dataset can provide basic data support for audio-visual cross-modal research; (2) The constructed audio-visual cross-modal matching models can provide a theoretical basis for audio-visual interaction technology; (3) In addition, the research method of audio-visual cross-modal matching proposed in this article can provide new research ideas for related research.