This paper designs a multimodal convolutional neural network model for the intelligent analysis of the influence of music genres on children’s emotions by constructing a multimodal convolutional neural network model and profoundly analyzing the impact of music genres on children’s feelings. Considering the diversity of music genre features in the audio power spectrogram, the Mel filtering method is used in the feature extraction stage to ensure the effective retention of the genre feature attributes of the audio signal by dimensional reduction of the Mel filtered signal, deepening the differences of the extracted features between different genres, and to reduce the input size and expand the model training scale in the model input stage, the audio power spectrogram obtained by feature extraction is cut the MSCN-LSTM consists of two modules: multiscale convolutional kernel convolutional neural network and long and short term memory network. The MSCNN network is used to extract the EEG signal features, the LSTM network is used to remove the temporal characteristics of the eye-movement signal, and the feature fusion is done by feature-level fusion. The multimodal signal has a higher emotion classification accuracy than the unimodal signal, and the average accuracy of emotion quadruple classification based on a 6-channel EEG signal, and children’s multimodal signal reaches 97.94%. After pretraining with the MSD (Million Song Dataset) dataset in this paper, the model effect was further improved significantly. The accuracy of the Dense Inception network improved to 91.0% and 89.91% on the GTZAN dataset and ISMIR2004 dataset, respectively, proving that the Dense Inception network’s effectiveness and advancedness of the Dense Inception network were demonstrated.
The intonation recognition of piano scores is an important problem in the field of music information retrieval. Based on the neural network theory, this study constructs a piano playing intonation recognition model and uses the optimized result as the feature of piano music to realize the prediction of the music recognition of the intonation preference. The model combines the behavioral preference relationship between intonation and musical notation to measure the similarity between intonations, which is used to calculate the similarity between intonation preference and music, and solves the quantification problem of intonation recognition. In the simulation process, the pitch preference feature of piano playing is used as the identification basis, and the effectiveness of the algorithm is verified through four sets of experiments. The experimental results show that the average symbol error rate of the improved network model is reduced to 0.3234%, and the model training time is about 33.3% of the traditional convolutional recurrent neural network, which is optimized in terms of recognition accuracy and training time in single-class pitch feature. In the recommended method of multi-category evaluation of pitch features, the recognition accuracy of multi-category pitch features is 42.89%, which effectively improves the musical tone recognition rate.
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