Robust fall detection using acoustic signals remains a significant challenge, primarily due to background noise and non-line-of-sight (nLoS) propagation in dynamic indoor environments. This paper presents AP-Fall, a novel system that combines both audible and inaudible acoustic signals for environment-adaptive fall detection. We propose a decision fusion framework that integrates the audible and inaudible feature streams based on the signal-to-noise ratio (SNR). Specifically, we apply a joint optimization method with the minimum mutual information (MMI) criterion to determine a set of optimal SNR partition boundaries and corresponding optimal stream weights. Experimental evaluations show that AP-Fall achieves an accuracy of over 93% in various challenging conditions, demonstrating its robustness and potential for real-world deployment on household audio devices.