Lung sound signal (LSS) measurements are taken to aid in the diagnosis of various diseases. Their interpretation is difficult however due to the presence of interference generated by the heart. Novel digital signal processing techniques are therefore proposed to automate the removal of the heart sound signal (HSS) interference from the LSS measurements. The HSS is first assumed to be a periodic component so that an adaptive line enhancer can be exploited for the mitigation of the HSS interference. The utility of the scheme is verified on synthetic signals, however its performance is found to be limited on real measurements due to sensitivity in the selection of a decorrelation parameter. An improved solution with multiple measurements, that does not require a decorrelation parameter and exploits the spatial dimensions, is therefore proposed on the basis of blind source extraction based upon second-order statistics. This approach is found to have improved performance on both real and synthetic datasets, although the level of departure from true periodicity impacts this improvement.