1. A processor implemented method ( 300 ) comprising: receiving a feature set (F) of a plurality of features associated with non-stationary audio signals ( 302 ); receiving a training set comprising a plurality of non-stationary clean audio signals (C) and non-stationary noisy audio signals (N) ( 304 ); generating a unique and distinctive feature set (UF) based on the training set and the feature set (F) ( 306 ); dynamically generating an unbiased threshold of unique feature attribute value (UFAV) and polarity (P) associated with each of the unique and distinctive features of the unique and distinctive feature set (UF) ( 308 ); identifying a test signal as non-stationary noisy test signal or non-stationary clean test signal by statistical isolation based on (i) a unique feature attribute value (UFAV) and polarity (P) associated with the test signal for each of the unique and distinctive features and (ii) the dynamically generated unbiased threshold of the unique feature attribute value (UFAV) and the polarity (P) associated with each of the unique and distinctive features of the unique and distinctive feature set (UF) ( 310 ); and classifying the test signal further as one of lightly noisy test signal and highly noisy test signal ( 312 ) based on one or more pre-defined conditions when the test signal is identified as the non-stationary noisy test signal.