A 1.54M-parameter CNN-RNN with blueprint separable convolutions, time-frequency BiLSTM/LSTM, and spatial/channel attention achieves state-of-the-art infant cry detection on a merged public dataset across SNRs down to -20 dB.
Multi-task learning for audio-based infant cry detection and reasoning,
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Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network
A 1.54M-parameter CNN-RNN with blueprint separable convolutions, time-frequency BiLSTM/LSTM, and spatial/channel attention achieves state-of-the-art infant cry detection on a merged public dataset across SNRs down to -20 dB.