Colour Classification Analysis Based on MFCC Acoustic Feature Sets and Machine Learning Algorithms in Sound–Colour Synaesthesia
Year: 2025
Type: article
Abstract: Sound–colour synaesthesia is a rare phenomenon in which auditory stimuli automatically evoke stable, subjectively real colour experiences. This study aimed to investigate whether the colours most frequently reported by a synesthete can be reliably predicted based on objective acoustic parameters of voice signals. The study analysed the responses of a 24-year-old blind woman to different voices, which she consciously associates with distinct coloured silhouettes. A classification analysis based on MFCC acoustic feature sets and machine learning algorithms (SVM, XGBoost) demonstrated that the models could be trained with very high Accuracy—up to 97–100% in binary classification and 89–90% in multi-class classification. These results provide new insights into how specific sound characteristics are linked to imagery arising from the human subconscious.
Source: Applied Sciences
DOI: 10.3390/app152212059
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416192444