Abstract: Musical note recognition is a very important research content in the field of music signal analyzing and processing.It provides a technical basis for automatic music transcription,musical instrument tuning,music database retrieval and electronic music synthesis.In the conventional note recognition method,the musical note of one-to-one correspondence is identified by estimating the fundamental frequency of the note and the standard frequency.However,one-to-one correspondence is more difficult to identify,and the error increases as the fundamental frequency of the musical note increases.And the identifiable musical note frequency range is not wide.To this end,the paper used the idea of classification for musical note recognition,and established the required musical note library.For the importance of the low frequency information of the music signal,the Mel Frequency Cepstrum Coefficient (MFCC) and the Constant Q Transform (CQT) are selected as the note signal extraction features.The extracted features MFCC and CQT are respectively input as a note recognition single feature,and the feature fusion input is performed.Combining the advantages of Softmax regression model in multi-classification problem and the good nonlinear mapping ability and self-learning ability of BP neural network,the BP neural network multi-classification recognizer is constructed based on Softmax regression model.In the simulation environment of MATLAB R2016a,the characteristic parameters were input into the multi-classifier for learning and training,and the optimal solution was found by adjusting the network parameters.The comparative experi-ment was performed by changing the number of training samples.The experimental result data shows that when the fusion feature (MFCC+CQT) is used as the feature input,25 types of notes from the big character group to the small character group can be identified,and the average recognition rate of 95.6% can be obtained.And the feature CQT has a greater contribution than the feature MFCC in the recognition process.The experimental data fully demonstrates that using classification ideas for musical note recognition can achieve good recognition results and is not limited by the range of the musical note’s fundamental frequency.

Key words: BP neural network, Constant Q transform, Feature fusion, Mel frequency cepstrum coefficients, Music note library, Softmax regression model