Abstract
Nowadays, with the rapid development of multimedia technology and computer information processing, the data of multimedia information presents explosive growth. At present, the method of using artificial recognition of sound materials is inefficient, and an automatic recognition and classification system of sound materials is needed. To improve the accuracy of sound recognition, two algorithm models are established to identify and compare the sound materials, which are the hidden Markov model (HMM) and back propagation neural network (BPNN) model. Firstly, HMM is established, and the sound material is randomly selected as the test sample. The comparison between the expected classification and the actual is tested, and the recognition rate of each classification is got. The final average recognition rate is 61%. The anti-interference characteristics of the training HMM are tested, and the identification rate of the training model is selected in 6 types of signal-to-noise ratio (SNR) environments. The recognition rate of the training model has an obvious downward trend with the decrease of the SNR. Secondly, the BPNN model is built, and 200 BPNN training experiments are conducted. The training model with the highest average recognition rate is selected as the experimental model. The average recognition rate of the final model is higher than 90%. The expression ability and stability of the trained model are simulated after the new sample is introduced, and the anti-interference performance of the model is tested in different environments of SNR. The results of performance test are good, and only the recognition rate of complex types of some sound sources decreased. Finally, the accuracy of the HMM in the experiment is not as high as that obtained by BPNN. Therefore, the training method of BPNN has a greater advantage in both recognition accuracy and recognition efficiency for the studied sound. It provides a reference for automatic recognition of sound materials.
Highlights
In the production of animation sound, the dubbing part requires a lot of sound materials
The input layer consists of 26 neurons, the middle layer consists of 13 neurons, and the output layer consists of 15 neurons
The hidden Markov model (HMM) is established, and the sound material is randomly selected as the test sample
Summary
In the production of animation sound, the dubbing part requires a lot of sound materials. It shows that the current research is basically in the stage of feasibility analysis or the effect of classification and recognition is not obvious enough, and the efficiency and accuracy of automatic classification and recognition of sounds need to be improved. For this reason, the idea of applying artificial intelligence (AI) components and multimedia technology to sound recognition is proposed, which can realize the automatic recognition of sound materials and avoid the problems of labor time and inefficiency. High-efficiency sound recognition is designed through ML algorithm to facilitate the classification of sound materials
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