Bark frequency cepstral coefficient based sadness emotion level recognition system

Based on the adverse physiological and social consequences associated with sadness, recognizing this emotion is crucial to mitigate its negative impacts. To address this issue, an efficient system was proposed for recognizing different level of sadness using embedded technology. The system captured speech input through a microphone and then processed it on Raspberry Pi microcomputer. The key feature extraction technique used was Bark-Frequency Cepstral Coefficient (BFCC) method. After selecting a set of features, a thorough analysis of the computational algorithms was conducted, and dimensionality reduction method was applied to reduce computational costs, including noise. These features served as a pre-processing stage for Machine Learning (ML) model based on K-Nearest Neighbors (KNN) architecture. The results were then shown on a smartphone application through Bluetooth connectivity. Based on experimental results, the system showed an impressive improvement of 11.97 dB in the signal-to-noise ratio (SNR) compared to feature extraction using Mel-frequency Cepstral Coefficient (MFCC) method. The results also showed a favorable accuracy rate, implying the effectiveness of KNN with a k-value of 5 in accurately identifying different level of sadness. The system particularly excelled in correctly recognizing high and low level of sadness. Therefore, the effective categorization of sadness emotion level could be applied to the early diagnosis of mental disorders such as depression and anxiety, contributing to the reduction of the adverse impact of sadness.