ROBUST detection of infant crying in adverse environments using weighted segmental two-dimensional linear frequency cepstral coefficients

This paper addresses the problem of automatically detecting infant crying sounds. Infant crying sounds show the distinct and regular time-frequency patterns that include a clear harmonic structure and a unique melody. Therefore, extracting appropriate features to properly represent these characteristics is important in achieving a good performance. In this paper, we propose weighted segment-based two-dimensional linear-frequency cepstral coefficients to characterize the time-frequency patterns within a long-range segment of the target signal. A Gaussian mixture model is adopted to statistically represent the crying and non-crying sounds, and test sounds are classified by using a likelihood ratio test. Evaluation of the proposed feature extraction method on a database of several hundred crying and non-crying sound clips yields an average equal error rate of 4.42% in various noisy environments, showing over 20% relative improvements compared to conventional feature extraction methods.