We present a sparse representation-based method for detecting adventitious lung sounds in low-quality auscultation signals. Since the noise cannot be represented sparsely by any bases, we can extract clear breath sounds and adventitious sounds from noisy electronic auscultation signals via the sparse representation. Using these clear sound components, we measure the level of abnormality, and robustly detect adventitious sounds with pulsating waveforms, a.k.a crackles. We have experimentally confirmed that our detection achieves an average precision of about 85 percents regardless of noise level.