We present a novel approach to detect infant cry in actual outdoor and indoor settings. Using computationally inexpensive features like Mel Frequency Cepstral Coefficients (MFCCs) and timbre-related features, the proposed algorithm yields very high recall rates for detecting infant cry in challenging settings such as café, street, playground, office, and home environments, even when Signal to Noise Ratio (SNR) is as low as 6 dB, while maintaining high precision. The results indicate that our approach is highly accurate, robust and, works in real-time.
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