We present our recent progress in ultra-low-power intelligent acoustic sensing that harnesses the high power and energy efficiency of cochlea-like analog feature extraction and binarized neural network classification. Compared with conventional methods including the fast Fourier transform-based feature extraction plus neural network classification, and the more aggressive approach based on end-to-end neural network models, the analog filter bank-based handcrafted feature extraction inspired by mammalian cochlea has the promise of achieving the minimum power consumption for many existing and emerging always-on audio applications. System considerations and circuit techniques that are used to achieve the high power efficiency will be presented and comparison with some state-of-the-art works, and future directions will be discussed.