Musical Instrument Timbre Recognition Based on Improved Deep Learning Algorithm
To achieve accurate identification of musical instrument timbres while preserving the temporal char-acteristics of their sound,a new approach based on an improved deep learning algorithm is proposed.This method commences by utilizing a one-dimensional convolutional neural network to extract features of musical instrument timbres,which leverages the combination of Mel-scaled filter bank log energies and Mel-Fre-quency Cepstral Coefficients.Then input the musical instrument timbre features into the musical instrument timbre classifier based on long short-term memory and deep neural network for musical instrument timbre rec-ognition.The results of the recognition tests conducted on the timbres of instruments from these five databases reveal that the proposed approach outperforms both traditional convolutional neural network-based timbre rec-ognition methods and hybrid convolutional neural network deep belief network approaches.Specifically,it a-chieves an improvement of 2.49%over convolutional neural network methods and 2.02%over hybrid convo-lutional neural network deep belief network methods,demonstrating its effectiveness in accurately identifying musical instrument timbres while preserving the inherent temporal dynamics of their sounds.
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