Research On Musical Instrument Classification Based On Deep Learning

The classification of musical instruments based on deep learning is the application of deep learning in the direction of music information retrieval,and music information retrieval is a hot research topic in recent years.First of all,the basic theoretical knowledge of music classification and deep learning was introduced to lay a theoretical foundation for the construction of the mixed model.Secondly,the two basic models of building deep learning hybrid models,DAE and DBM,were introduced,including the basic structure,training algorithm,performance characteristics and research status of the model.A hybrid model of DA-BM deep learning which based on the data projection capability of DAE and expansion ability of DBM is constructed.To alleviate the turbulence in model training by adding momentum parameter in the process of adjustment,and the introduction of mean field method to smooth outlier in the data training,with lifting the robustness of model.DA-BM acted as a feature extractor in the classification task of musical instruments,and set up SVM and Softmax classifier on the top of the model,and then constructed a mixed classification model of DA-BMSV and DA-BMSM.Finally,the effectiveness of the proposed DA-BM hybrid model is verified by simulation experiments.In addition,based on the PLP coefficient,the characteristics of human ear hearing can be simulated,and the characteristics of the energy change between two adjacent frames in the music signal can be reflected by spectral flux,and a new hybrid acoustic characteristic was proposed.The DA-BM model was applied to the classification task of 5 kinds of single musical instrument audio data,and the performance was compared with traditional music classification method and single deep learning model.The experimental results show that the classification accuracy of the mixed deep learning model DA-BM is higher than the single deep learning model DAE and DBM on the same test data set.At the same time,the performance of the three deep learning models is obviously better than the shallow model SVM in the instrument classification task,and the accuracy of classification is increased by more than 10%.In addition,the energy features,MFCC coefficients and PLP coefficients of the music data were extracted,and the mixed features based on PLP coefficients and spectral fluxes were constructed.The above characteristics were used as input of DA-BM model respectively,and the performance of different characteristic quantities was compared.The experimental results show that the mixed characteristic quantity is the best and the classification accuracy reaches 91.25%.It is proved that the mixed feature quantity proposed in this paper is better to express the data.