Music recognition using note transition context

As a typical example of sound-mixture recognition, the recognition of ensemble music is addressed. Here music recognition is defined as recognizing the pitch and the name of an instrument for each musical note in monaural or stereo recordings of real music performances. The first key part of the proposed method is adaptive template matching that can cope with variability in musical sounds. This is employed in the hypothesis-generation stage. The second key part of the proposed method is musical context integration based on the probabilistic networks. This is employed in the hypothesis-verification stage. The evaluation results clearly show the advantages of these two processes.