This paper proposes and evaluates a perceptual model for the measurement of “punch” in musical signals based on a novel algorithm. Punch is an attribute that is often used to characterize music or sound sources that convey a sense of dynamic power or weight to the listener. A methodology is explored that combines signal separation, onset detection, and low level feature measurement to produce a perceptually weighted punch score. The model weightings are derived through a series of listening tests using noise bursts, which investigate the perceptual relevance of the onset time and frequency components of the signal across octave bands. The punch score is determined by a weighted sum of these parameters using coefficients derived through regression analysis. The model outputs are evaluated against subjective scores obtained through a pairwise comparison listening test using a wide variety of musical stimuli and against other computational models. The model output PM95 outperformed the other models showing a “very strong” correlation with punch perception with both Pearson r and Spearman rho coefficients being 0.849 and 0.833 respectively both being significant at the 0.01 level (2-tailed).