On stable learning of block-diagonal recurrent neural networks, part 2: application to the analysis of lung sounds

For pt.1, see ibid., vol. no.2, p815-20 (2004). A recurrent neural filter for the separation of discontinuous adventitious sounds from vesicular sounds is presented. The filter uses two block-diagonal recurrent neural networks to perform the task of separation and is trained by the RENNCOM training algorithm. Extensive experimental results are given and performance comparisons with a series of other models are conducted, underlining the effectiveness of the proposed filter.