Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body's most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step we provided a test signal to the system, at the edge of the hearing threshold. As next step we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.