| Title |
Colour classification analysis based on MFCC acoustic feature sets and machine learning algorithms in sound–colour synaesthesia |
| Authors |
Bartulienė, Raminta ; Ragaišė, Diana ; Maciulevičius, Martynas ; Raišutis, Renaldas ; Davidavičius, Gustavas ; Saudargienė, Aušra ; Šatkauskas, Saulius |
| DOI |
10.3390/app152212059 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2025, vol. 15, iss. 22, art. no. 12059, p. 36-44.. ISSN 2076-3417 |
| Keywords [eng] |
chromesthesia ; classification ; machine learning ; MFCC acoustic feature sets ; synaesthesia |
| Abstract [eng] |
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. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
