Robust unsupervised detection of human screams in noisy acoustic environments

This study is focused on an unsupervised approach for detection of human scream vocalizations from continuous recordings in noisy acoustic environments. The proposed detection solution is based on compound segmentation, which employs weighted mean distance, T²-statistics and Bayesian Information Criteria for detection of screams. This solution also employs an unsupervised threshold optimized Combo-SAD for removal of non-vocal noisy segments in the preliminary stage. A total of five noisy environments were simulated for noise levels ranging from -20dB to +20dB for five different noisy environments. Performance of proposed system was compared using two alternative acoustic front-end features (i) Mel-frequency cepstral coefficients (MFCC) and (ii) perceptual minimum variance distortionless response (PMVDR). Evaluation results show that the new scream detection solution works well for clean, +20, +10 dB SNR levels, with performance declining as SNR decreases to -20dB across a number of the noise sources considered.