| Abstrakt: |
Infant cry classification is an important area of research that involves distinguishing between normal and pathological cries. Traditional feature sets, such as Short-Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCC) have shown limitations due to poor spectral resolution caused by quasi-periodic sampling in high pitch-source harmonics. To address this, we propose to use Constant-Q Cepstral Coefficients (CQCC), which leverage geometrically-spaced frequency bins for improved representation of the fundamental frequency ($F_{0}$) and its harmonics for infant cry classification. Two datasets, Baby Chilanto and In-House DA-IICT, were employed to evaluate the proposed feature set. We compared the CQCC against state-of-the-art feature sets, such as MFCC and Linear Frequency Cepstral Coefficients (LFCC) using Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers, with 10-fold cross-validation. The CQCC-GMM architecture achieved relatively better accuracy of 99.8% on the Baby Chilanto dataset and 98.24% on the In-House DA-IICT dataset. This work demonstrates the effectiveness of CQCC's form-invariance over traditional STFT-based spectrograms. Additionally, it explores parameter tuning and the impact of feature vector dimensions. The study presents cross-database and combined dataset scenarios, yielding an overall performance improvement of 1.59%. CQCC's robustness was also evaluated under various signal degradation conditions, including additive babble noise at different Signal-to-Noise Ratios (SNR). The performance was further compared with other feature sets using statistical measures, including $F1$-score, J-statistics, and latency analysis for practical deployment. Lastly, CQCC's results were compared with existing studies on the Baby Chilanto dataset. |
