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This paper digs out the corresponding influencing factors by defining the traditional music communication effect. For research purposes, the questionnaire is analyzed to analyze the impact of traditional music communication from an Internet perspective. Two distribution methods, online and offline, are used to collect research data. The data are analyzed based on the Pearson correlation coefficient and multiple regression model, after testing the reliability of the questionnaire. The music form scored the highest, with a mean of 20.70 and a standard deviation of 12.09, and it had the greatest influence on the traditional music dissemination process. There is a significant positive correlation between music content, music form, creator, communicator, audience, and communication media and traditional music communication effect, P

Traditional music is an important carrier for the development and dissemination of Chinese culture, and this paper aims to use multimedia technology to broaden the inheritance of traditional music and put forward the modernized inheritance of traditional music. In order to solve the limitation of time and space of this inheritance mode, it is proposed to utilize the main melody extraction technology of MIDI music to extract and identify the humming melody for the inheritance mode of traditional music. To facilitate the creation and retrieval of traditional music, a traditional music humming retrieval system based on the CHMM model has been designed by improving the HMM model based on score and audio training. Set the neural network model’s parameter values and bound values and use the multimedia system in teaching to test its effectiveness. The algorithm in this paper stabilizes the extraction error value for MIDI music melody at a value between 0.10 and 0.18. Combined with the school’s use of multimedia inheritance technology for traditional music, more than 70% of the students think that the teaching of traditional music using modern multimedia technology is very good. Only 0.01685 students expressed uncertainty about the significance of multimedia in traditional music teaching. The evolution of multimedia technology in traditional music transmission is evident.

In the era of informationization, the inheritance and development of traditional music are facing challenges and opportunities. This study analyzes the problems faced by teaching the basic theory of traditional music and proposes a coping strategy for constructing an interactive teaching model and platform for informatization. In the interactive teaching model, P2P technology is used to design and construct the interactive behavior model and interest analysis module, and fuzzy theory is used to precisely set the key information weights. The informationized teaching platform is designed with AI technology, adopting the recommendation strategy of a hybrid recommendation model combined with collaborative filtering, using cosine similarity to calculate the content similarity, and combining with artificial intelligence technology to realize music teaching quality assessment. In the traditional music basic theory teaching practice, S High School is used as a practice site, and experimental and control classes are set up to conduct teaching. The experimental class perceived negative behaviors such as “harshness” and “dissatisfaction” as lower than the control class, and the pass rate and excellence rate of the post-test of traditional music basic theory reached 92.5% and 36.9%, respectively. The mean values of all other music core literacy dimensions except for knowledge of opera and general knowledge of musical instruments were higher than those of the control class, showing significant differences (P

Traditional music encapsulates the profound historical wisdom and cultural imprints of a nation, representing an invaluable asset within the enduring heritage of its people. It also serves as an essential resource for music education in higher education institutions. This study aims to delineate the methodologies for the productive incorporation of traditional music into the music curricula of colleges and universities. Utilizing a questionnaire survey, this research examines the current practices of integrating traditional music into academic settings while concurrently identifying and analyzing prevalent challenges encountered in this endeavor. A factor analysis was conducted to extract the principal components from the identified challenges, providing a structured understanding of the influencing factors. The findings from this study suggest that the primary determinants impacting the integration of traditional music into university music programs include the pedagogical approaches of educators, the educational environment, and the prevailing campus culture. Furthermore, the study proposes several effective strategies for the integration of traditional music into music education, which include fostering emotional connections with the music, emphasizing scientifically backed vocal techniques, and incorporating exemplary cases into the curriculum. These strategies aim to enhance the educational experience and cultural appreciation among students, thereby enriching the music education landscape in higher educational settings.

With the development of information technology, music teaching methods are getting more affluent and more prosperous. This paper proposes a model for emotion classification and assessment that integrates traditional music culture elements with information technology in music teaching. The research first combines TextCNN and BiLSTM algorithms to establish the emotion classification model of conventional music. Then it combines PYIN and DTW algorithms to establish the evaluation model of traditional music, which completes the auxiliary efficacy of music informationized teaching. In the emotion classification test of the model, the classification accuracy and F1 value of emotions of different music samples are 82.98% and 75.22%, respectively. The model’s recognition accuracy of the four voices is 86.76%, and the overall effective scoring percentage is 81.98% under different playing abnormalities. This study has had an impact on traditional music evaluation. The model in this paper can be used to classify and evaluate emotions in conventional music, providing more intelligent and high-quality technical services for integrating traditional music into music teaching.

This study proposes an intelligent teaching model for music that incorporates traditional music culture and scientifically evaluates learning outcomes through a simulated annealing algorithm. The algorithm determines the initial temperature by adaptive function transformation, which enhances its applicability and generalization. The music intelligent teaching model is applied in actual teaching and data are collected by comparing and analyzing the learning outcomes of two parallel classes using questionnaires and statistical methods. The results of the experiment showed that the mean scores of the students in the experimental class were significantly better than those of the control class in terms of linguistic intelligence (P=0.042) and musical intelligence (P=0.003), indicating the effectiveness of the teaching model. The most significant advantage of the model is that it allows students to learn anytime, anywhere, free from the constraints of time and space, which amounted to 60.8%. However, its main limitation was the tonal differences in digital course recordings. Overall, this study provides new ideas and methods for intelligent teaching of music integrating traditional cultural elements, which is of great significance for improving the quality of education.

In this study, Gaussian mixture model preprocessing and histogram of orientation gradient (HOG) and histogram of optical flow direction (HOF) action feature extraction techniques are used to significantly optimize the accuracy of dance performance action recognition. In addition, the VNect pose estimation algorithm and dual-stream 3D-CNN-based skeleton action recognition are used to improve significantly the accuracy and efficiency of recognition. In terms of music feature recognition, this study utilizes short-time time domain and frequency domain analysis techniques and BP neural networks to effectively achieve accurate recognition and sentiment classification of music features. Analyzing a dataset containing 6000 training videos, 800 validation videos, and 1200 test videos, the study reveals that musical elements, melody, rhythm, emotional tone, and song style significantly impact the stage performance effect. The study not only highlights the importance of traditional Chinese music in dance performance to enhance the artistic effect and deepen the emotional expression, but also opens up new perspectives and methods for disseminating and developing traditional culture.

This article explores the application of digital information technology in music education, especially in teaching traditional music. Taking the intersection of conventional music culture and applied teaching practice, the article utilizes advanced algorithmic techniques - Support Vector Machines and Apriori Association Rule Algorithm - to construct an innovative model of emotional cognition and stylistic categorization of traditional music. This model integrates standard music emotion cognition and style classification into modern informationized music teaching. To verify the model’s effectiveness, the researchers selected 500 pieces of traditional Chinese music as samples, trained and tested using the constructed model, and compared it with other algorithmic models. The test results show that the model outperforms BP neural networks and Linear neural networks regarding emotion perception. Specifically, the model shows efficient performance with mean square error (MSE) and correlation coefficient (CC) averages of 0.0099 and 90.50% on the test set, respectively. In addition, the model has the shortest processing time and is lower than the IM-Apriori algorithm regarding space consumption, showing its efficiency and resource-saving advantages. The model’s accuracy ranges from 75.23% to 90.06% in classifying traditional music. In summary, the model proposed in this paper improves the accuracy and efficiency of emotional cognition and style classification of conventional music and provides essential technical support for effectively integrating traditional music into modern informative music teaching.

This paper uses the ARIMA model in time series and the BLSTM sentiment classification algorithm in sentiment analysis to predict the elements and the direction of traditional music heritage. Differential processing of non-smooth series data stabilizes the time series data of traditional music inheritance. By extracting all the features contained in the sheet music, the interpreted sheet music is subjected to sentiment analysis to further analyze the inheritable elements of traditional music. The results show that the time series model has high accuracy in predicting the inheritable elements of traditional music, the MAPE value of the ARIMA model is 5.9658308, and the melody, as well as the structure of traditional music, can be integrated into piano teaching in colleges and universities to a certain extent, with the integration degree of the melody being 0.72 and the integration degree of the structure is 0.655.

In this paper, we first propose a Markov chain-based automatic composition method to construct a song set using classical and traditional music as a sample. Using probability matrices, experiments are conducted to generate pitch and time value sequences, and melodic segments of traditional music are generated using transformation techniques. Secondly, a traditional music digital resource library that integrates multiple functions is constructed by utilizing various information technologies. Finally, based on the existing classification dimensions, the relationship between various types of influencing factors in the inheritance of traditional music was analyzed, and the structure of the questionnaire was initially constructed. The results show that the correlation coefficients between the cultural inheritance of traditional music and its resources, environmental threats, development level, diversified values, public demand, and social cognition are 0.4235, 0.0953, 0.3594, 0.4253, 0.5348, and 0.5224, respectively, and there is a significant positive influence. This paper provides some theoretical and practical guidance for the protection and inheritance of traditional music.

In this paper, we first start from extracting music features and analyze the extraction methods for time-domain, frequency-domain, and cepstrum-domain features of music. Next, the logistic regression model is used to recognize music and deal with two-class and multi-class classification problems. To extract music and text features, a CNN-SVM-based classification model is proposed using a deep learning teaching practice structure. Finally, feature selection experiments are carried out on the extracted raw feature set using various feature selection algorithms to optimize the feature set and reduce classifier computation. The results show that the average number of feature items selected using the improved correlation coefficient method is 64, and the number of feature items is mostly distributed between 58 and 72. The data verifies that the improved correlation coefficient method proposed can effectively extract the features of traditional music, improve the accuracy of traditional music classification, and thus promote the development of the fusion of traditional music culture and vocal music teaching in colleges and universities.

The first part of this paper examines the classification of traditional music culture and the method of integrating it with modern vocal teaching. Then, the association rule algorithm was used to establish the calculation model of integrating college vocal teaching and traditional music culture, and the weighted MINWAL algorithm improved the model. Finally, the degree of integration and the effect of integration between college vocal music teaching and traditional music culture were analyzed using indicators of integration degree, value, and acceptance. The fusion levels of singing style, voice, tune melody, lyrics, performance technique and dynamics were 4, 3.5, 4.3, 4.4, 4.1 and 3.9, respectively. The fusion level regarding teaching content was 4.1, and the fusion level in terms of practical activities was 4.0. A teaching strategy that is feasible and effective and has promotion value is the combination of college vocal music teaching and traditional music culture.