This paper investigates the pitch estimation and the instrument recognition of music signals. A note exemplar is a spectrum segment of notes of the specific pitch and instrument, which is stored as a form of dictionary preliminarily. We describe the method of reconstructing a frame of musical signals as the linear combination of exemplars from the large exemplar dictionary with sparse (l1 minimized) coefficient vector. Reconstruction constraints are imposed to KL divergence of spectra, which is found to produce better results than Euclidean distance. The proposed algorithm shows the ability to transcript music pieces with relatively many notes per a frame and to divide the instrument explicitly through some experiments.