The phonocardiography (PCG) is an important technique for the diagnosis of several heart conditions. However, the PCG signal is highly prone to noise, which can be an obstacle for the detection and interpretation of physiological heart sounds. Thus, the detection and elimination of noise present in PCG signals is crucial for the accurate analysis of heart sounds, especially in p-health environments. Noise can be introduced by various internal factors (e.g., respiration and laughing) and by external conditions (e.g., phone ringing or door closing). To mention also that the noise frequency components are typically overlapped with the PCG spectrum, increasing the complexity of the analysis. The purpose of the present work consists in the detection of noisy periods willfully introduced during the performance of three different sets of tasks. The developed method returns the classification of the signal content, in a window-by-window analysis and can be divided in two distinct phases. The first step consists in the search for a noise free window using a feature obtained from the PCG time-domain. In the second step, the noise free window is compared with the remaining signal. The classification between clean and contaminated PCG is performed using two features from the frequency domain. The algorithm was able to discriminate clean from contamined PCG sections with an average sensitivity and specificity of 95.59% and 92.68%, respectively.