To resolve the prior dependency of existing enhancement algorithms based on dictionary learning, an unsupervised self-learning approach for speech enhancement in one channel record is presented. Firstly, the algorithm decomposes the magnitude spectrum of noisy speech efficiently into low-rank part, sparse part and noise part. Then, the dictionary of noise is acquired by learning the low-rank part. Finally, the clean speech is separated by using the acquired noise dictionary and multiplicative update rules. As the approach is unsupervised, it is more convenient and practice than other enhancement methods based on dictionary learning. The experiment results show that the approach proposed outperforms other enhancement methods like robust principal component analysis and multiband spectra subtraction in terms of harmonic structure maintaining and noise suppression.