The elderly people living alone or life of a patient face distress situations particularly in case of falling and becoming unable to ask for help. Fall in elderly people may result in head injury, broken hips, and bones that need immediate hospitalization to lower the mortality risk. During the last decade, several technological solutions were presented for early fall detection but most of them have critical limitations and are impeded by several environmental constraints. In this paper, we have analyzed the environmental sounds for early fall detection utilizing the fact that reflection of pain directly occurs through sound. The proposed framework first analyzes the environmental sounds by suppressing the silence zones in signals and distinguishing overlapping sound signals through hidden Markov model based component analysis (HMM-CA). The source separated components are then represented by acoustic local ternary patterns (acoustic-LTPs) by extending the existing ideas of acoustic local binary patterns (acoustic-LBPs). In the proposed work, we have also introduced the concept of rotation invariance through uniform patterns for audio signals that, arguably, is a fundamental requirement for an acoustic descriptor. Once the signal representation is completed, we classify the signals through SVM classifier. The performance of the proposed acoustic-LTP is evaluated against state-of-the-art methods and acoustic-LBP. Results clearly evince that proposed method is more powerful and reliable in terms of fall detection when compared against other methods.