College Music Education Research Articles

Analysis and Innovation of Vocal Music Teaching Quality in Colleges and Universities Based on Artificial Intelligence

Abstract The collision between the field of education and the field of artificial intelligence has helped the wisdom reform of college classrooms. At present, traditional vocal music teaching is inefficient and difficult to manage, but it is faced with the dilemma of resource shortage. Therefore, to improve the quality of classroom vocal music teaching in colleges and universities, using innovative thinking, this paper intends to design an artificial intelligence system for evaluating and analyzing classroom effects. Use the test vocal music characteristic parameters for comparison, through the analytic hierarchy process to calculate the quality of the classroom teaching evaluation index, and auxiliary management of the vocal music teaching process. The final simulation results are in line with expectations. It can not only evaluate the whole teaching process reasonably but also help teachers improve the quality of vocal music teaching, which has strong practical value.

Research on the practice of national vocal music teaching in colleges and universities under the background of firmly forming the consciousness of Chinese national community

Research on the practice of national vocal music teaching in colleges and universities under the background of firmly forming the consciousness of Chinese national community

Research on College Music Teaching and the Inheritance of Ethnic Music Culture

Research on College Music Teaching and the Inheritance of Ethnic Music Culture

The Application of Multiple Music Cultures in College Music Teaching in the Background of Internet

Abstract In advancing quality education, there is a pressing necessity to equip students in higher education with global perspectives and skills in intercultural communication. This study leverages the TSPCK knowledge transformation model to deliver an extensive exploration and application in the realms of goal orientation, content structuring, foundational learning, strategy deployment, and the execution of assessments within the framework of multicultural music education. The adaptive learning system’s learner model is meticulously crafted by assessing learners’ preliminary knowledge, evaluating their challenges with music knowledge, and estimating their overall knowledge proficiency, with validation conducted across six distinct datasets. Multicultural elements in college music education are strategically utilized to investigate their influence on students’ aesthetic interests and musical literacy. Empirical results indicate a residual mean score of 2.0501 for music aesthetic literacy within a multicultural context, suggesting that exposure to multicultural music education broadens students’ perspectives significantly. Furthermore, this educational approach encourages students to cultivate a deeper appreciation and respect for the nuances and commonalities across various cultures. Enhancing the quality of music education in tertiary institutions and promoting a profound understanding and proficiency in diverse music cultures are identified as pivotal elements in achieving these educational outcomes.

Emotion Appreciation Strategy in College Music Teaching Based on Improved Multimodal RCNN

Abstract People’s judgment of music emotion is highly subjective; how to quantify the music emotion characteristics is the key to solving the music emotion recognition problem. This paper utilizes the Fourier transform method to preprocess the input music sample signal. A digital filter accomplishes the pre-emphasis operation, and the number of frames in the music signal is determined by splitting and windowing through a convolution operation. By utilizing the Mel frequency cepstrum coefficient and cochlear frequency, emotional features of music can be extracted. Improve the multimodal model based on the RCNN algorithm, propose the TWC music emotion framework, and construct a music emotion recognition model that incorporates the improved multimodal RCNN. The proposed model’s impact on music emotion appreciation is evaluated through experiments to identify music emotions and an analysis of college music teaching practices that emphasize emotion appreciation. The results show that 1376 songs belonging to the category of “relaxation” are assigned to the category of “healing”, which is only 4 songs short of the target, and the labeling of the songs is not homogeneous, and the emotional recognition of the model is consistent with the cognition. The mean value of the empathy ability of college students in music emotion appreciation is 69.13, which is in the middle-upper level, indicating that the model proposed in this paper has a good effect on the cultivation of students’ music emotion appreciation.

Research on the Integration Strategy of College Music Teaching and Traditional Music Culture in the Context of Internet Plus

Abstract The integration of traditional music culture into music education in higher education institutions is crucial for enhancing students’ musical literacy and cultural refinement and fostering cultural self-confidence. This paper addresses the integration of music teaching with traditional music culture, employing a coupled coordination degree model based on the CRITIC method to quantitatively assess this integration at a specific music college. Key influencing factors were identified across seven dimensions and were used as condition variables in a fuzzy-set Qualitative Comparative Analysis (fsQCA). The analysis indicates that the current level of integration at the institution is critically low, categorized as “endangered dysfunction,” with an average coupling coordination degree of 0.478. The fsQCA identified four distinct pathways, accounting for approximately 82.6% of the variance in integration levels. To enhance the integration of music teaching and traditional music culture effectively, institutions should focus on several strategic dimensions: curriculum development, instructional materials, interactive teaching methods, technology integration, faculty development, the incorporation of traditional music cultural practices, and the promotion of scholarly research in traditional music culture. This multifaceted approach promises to significantly advance the integration process, benefiting both educational outcomes and cultural preservation.

Application of auditory perception technology to multimodal teaching innovations in college music

Abstract This study explores a university music teaching system enhanced by auditory perception technology. It delves into the intricacies of auditory perception technology and its integration with multimodal music education, highlighting the potential applications in university settings. Using short Fourier transform and wavelet transform techniques, the system computes Mel frequency cepstrum coefficients (MFCCs) and first-order differential dynamic music characteristics. These are then utilized to construct the multimodal teaching framework through computer programming languages. The multimodal music teaching system was tested and analyzed using data analysis software. The results showed that the experimental group and the control group produced significant differences (P<0.05) in the four aspects of fluency (0.005), flexibility (0.003), originality (0.001), and the total score of singing skills (0.004) of music singing skills. This study not only enriches theoretical research on multimodal teaching innovations in music but also promotes the development of university music education.

Reform of University Music Education under Ethnic Music Culture

Reform of University Music Education under Ethnic Music Culture

Changes in Music Curriculum Education in Colleges and Universities Based on Cloud Classroom Assistive Technology

Abstract With the rapid development of science and technology, cloud classroom teaching has gradually become a trend. The cloud-based music teaching aid training system designed in this paper learns to map rhythmic and pitch features through music sequence generation technology. The onset detection algorithm of spectral flux detects the onset of the note. The Bezier curve is introduced into the melodic line feature generation algorithm to construct a polynomial function for interpolation between the first and the last endpoints, and the emotion is utilized to guide fractal music creation. Finally, the effectiveness of this auxiliary teaching system in college music teaching has been verified. The results show that the optimal Hamming distance of the system is 0.353 in the music melody generation experiment, which indicates that the system can quickly generate melody lines for students in music teaching and improve their learning efficiency. The system can significantly improve the rationality and efficiency of music skill training, which is worth promoting.

Research on the Innovation of Piano Music Teaching in Colleges and Universities Based on the Characteristics of Music Culture

Abstract Starting from the communication characteristics of piano music culture in colleges and universities, this paper proposes a piano music signal detection technology that includes two parts: note onset and fundamental frequency detection. Based on the characteristics of the harmonic structure of the music signal, the degree of harmonic matching between the signal to be recognized and the standard note signal is calculated, and the task of multi-fundamental frequency estimation is accomplished by using the timbre characteristics and harmonic structure. The detection technology of piano music signals is applied to the recognition of teaching short notes, playing music and playing evaluation to get a new model of piano music teaching in colleges and universities. The results show that the algorithm in this paper can recognize piano music correctly by more than 91%, and the method has no obvious decrease in recognition rate in the process of gradually increasing noise, which is conducive to improving the effect of piano music teaching in colleges and universities.

An explanatory sequential pilot inquiry on music therapy and performance anxiety in university music education majors

Music Performance Anxiety (MPA) is prevalent among amateur and professional musicians. While music therapy has established itself as an effective therapy for reducing anxiety in numerous individuals with varying health issues, in depth research on the potential benefits of music therapy for managing MPA is lacking. This explanatory sequential pilot inquiry with 15 undergraduate music education majors from two large music faculties in Canada assessed the benefits of group music psychotherapy offered for 6 weeks in person or via telehealth on the experience of MPA. Test measures included the Spielberger State and Trait Anxiety Inventories (STAI) pre and post study, a pre and post study questionnaire, pre and post music psychotherapy session Likert scales for anxiety and mood, and a post study interview. Music psychotherapy interventions included breathing exercises, grounding and guided visualizations/meditations, and musical improvisation. Quantitative data, assessed for all participants combined and by group, revealed that anxiety and mood scores on the Likert scale improved after participation in six music psychotherapy sessions, while the STAI scores remained constant pre and post study. Qualitative themes suggest the benefits of music psychotherapy for changed perception and cognitions, sense of support, reduction in symptoms and overall continued and future management of MPA. The results of this study are promising, pointing to music psychotherapy as a potential short-term therapy for MPA symptom management, having practical implications for post-secondary music education and future research.

Retracted: Research on the Innovation of Music Teaching in Universities Based on Artificial Intelligence Technology

Retracted: Research on the Innovation of Music Teaching in Universities Based on Artificial Intelligence Technology

The Integration of Instrumental Elements and Vocal Music Teaching in Colleges and Universities Based on the Core Literacy View of Music

Abstract In this paper, Fourier transforms in big data technology is used to realize the data preprocessing process of vocal signals from analog signals to digital signals and to explore the identification law of various music styles in vocal teaching in colleges and universities. According to the application of a convolutional neural network in vocal music teaching, the evaluation model of vocal music teaching based on a convolutional neural network is constructed by utilizing a convolutional neural network with accurate sensitivity and high-speed eigenvalue training speed. Starting from the design of relevant variables and the selection of evaluation tools, the experimental study of emotional therapy for the integration of percussion instruments and vocal music teaching is designed, and statistical analysis and simulation analysis are used to analyze the integration of musical instruments and vocal music teaching in the context of music core literacy. The results show that the evaluation error of the recurrent neural network method is 10.07%, while the minimum value is 0.24%. The maximum evaluation error of the convolutional neural network method is 8.22%, and the minimum evaluation error is 0.22%. The evaluation results of the quality of the integrated teaching of percussion instruments and vocal music of the model in this paper are better than the evaluation method of recurrent neural networks. During the maintenance period, when subject three was given weekly emotional therapy for teaching percussion instruments and vocal fusion, it was found that the subject’s emotional behavioral problems also completely subsided. This study realizes that the elimination of negative emotions in the teaching of musical instrument elements and college vocal music has a facilitating effect on the formation of students’ core musical literacy perspective.

Informatization Integration Strategy of Modern Popular Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

Abstract In this paper, a music teaching aid is constructed based on the automatic analysis technique of music elements. The auxiliary tool uses constant Q transform and note onset detection technology to process and recognize students’ input audio. The logical operation of music longitudinal structure and spectral structure and the logical conversion of beat and rhythmic time nodes are used to realize the integration of popular music and traditional music teaching informationization. Using two corpora of 22 kHz, the effect of teaching the fusion of musical elements of auxiliary tools was analyzed with the regular transformation of curves in the sound intensity envelope. The results showed that the peaks of the overlap between elements of popular music and traditional music appeared at 56.8 FPs, 148.9 FPs, and 343.7 FPs, respectively, corresponding to the peaks of 48.3 Hz, 59.4 Hz, and 51.6 Hz, respectively. The integration of traditional music culture into popular music teaching was successfully realized. This study effectively examined an effective method and strategy for integrating teaching, which has a positive impact on the promotion and development of traditional music culture.

Informatization Integration Strategy of Modern Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

Abstract This paper utilizes deep learning algorithms to informally integrate modern vocal music teaching with traditional music culture and extracts audio time-domain features and frequency-domain features through neural network self-learning. Secondly, a large number of music tracks are decomposed into music patterns, which constitute a music pattern library, and a music training model is generated through the automatic music audio synthesis algorithm based on a recurrent neural network, and the GRU model is used for music training and model prediction. The strategy of integrating artificial intelligence and modern vocal music teaching mode through traditional music culture in modern vocal music teaching is informatized, and a controlled experiment is carried out with H Music Academy as an example. The results show that the average degree of completion of the learning objectives of the students in the two experimental classes is 89.32 and 87.16, respectively, which is 14.15 and 11.99 higher than the average degree of completion of the control class. This study demonstrates that the teaching mode of traditional music culture integration in modern vocal music teaching can enhance the student’s ability of vocal music skills and practically improve the students’ artistic literacy, which can improve the degree of completion of the student’s learning objectives and in turn, improve the overall level of vocal music teaching.

Construction of college music information teaching mode under the background of Internet

Abstract The traditional music teaching method in the informationization era has been difficult to adapt to the needs of modern teaching and must be reformed in the direction of informationization. In this paper, based on the closure, inflection point and outer enclosing box features of the stroke line element, the recognition of handwritten notes is carried out from the three categories of straight line segments, folded line segments and quadratic curves. Meanwhile, for the binarized music score image, the multi-directional LBP features for spectral line detection are improved, and the computation method of multi-scale spectral line detection LBP features is established. The Manhattan distance is used to evaluate and select the features, which are inputted into XGBoost for classification and recognition training based on the statistical distribution characteristics of the features. Note recognition and spectral line recognition are applied to college music teaching, and the effectiveness of teaching is explored. In the rhythm-recognition path, the recognition teaching based on multi-scale and multi-directional LBP features led to an increase in students’ mastery of the musical score by 2.8 and in the phrasing and segmentation path by 3.5. Informational teaching led to a deepening of students’ mastery of the notes and musical scores.

Research on the Integration Path of College Vocal Music Teaching and Traditional Music Culture Based on Deep Learning

Abstract 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.

Reform and Innovation of Teaching Music Curriculum in Colleges and Universities by Applying Cognitive Mapping

Abstract This paper proposes a teaching innovation based on cognitive mapping to address the current problem of dull music teaching in colleges and universities. The combination of BERT-weighted sentence vectors and IDCNN is used to label music topics in cognitive mapping, while music emotion labeling is achieved using BiGRU-Attention. The above music labeling is integrated into the construction of a teaching-oriented cognitive map, and a learning path planning method based on the RippleNet algorithm is proposed under the cognitive map. The teaching effect under the application of cognitive mapping was evaluated in terms of basic literacy and emotional expression after verifying the applicability of the learning path using DCG indicators. As a whole, the student’s basic music literacy improved by an average of 5.16 points in the first stage and by an average of 7.67 points in the whole stage analyzed. The fastest improvement was in Sample 22, which improved by 9.9 points in the first stage and another 3.7 points in the second stage. Learning path planning with the help of cognitive mapping is feasible in the reform of music teaching in colleges and universities.

Using cognitive mapping to analyze the aesthetic value of music education in colleges and universities

Abstract In this paper, firstly, on the basis of cognitive mapping, music data are processed in four directions, namely, sound spectrum extraction, audio superposition, audio tempo, and tone intensity adjustment, and then audio sequences are sliced and diced, so that the cognitive mapping can be more focused on capturing localized useful information. Then, the BERT model is used to encode the music sequences so that the output music sequences already contain aesthetic and emotional features and the cognitive mapping-based music emotion analysis model is constructed according to the music emotion learning layer, the emotion feature aggregation layer, and the full connectivity layer, and analyzes the aesthetic value of music education in colleges and universities. According to the results, the BERT model had a difference of 0.13% on the MED dataset and 0.03% on the MTD dataset. On the mean value of the aesthetic value of traditional music, introverted personality (3.61) was higher than extroverted personality (3.52). This study has a positive effect on the cultivation of aesthetic value in music education in colleges and universities and proposes effective ways to enhance aesthetic value in college and university music teaching.

Strategies for the Integration of Traditional Music Culture in College Music Teaching under Information Diffusion Modeling

Abstract The diffusion degree of traditional music in music teaching is analyzed by constructing an information diffusion model based on traditional music knowledge. Combine the information flow window to analyze the path process of integrating traditional music culture and music teaching. Combine the kernel function to calculate the degree of similarity between traditional music culture and music teaching. Analyze the students’ preference for traditional music culture content in music teaching by calculating the traditional music theme preference value. The results show that music teaching that incorporates traditional music culture has a better diffusion effect on teaching content, teaching method and teaching form, with the diffusion degree in the interval of [0.45, 0.85), and the diffusion degree interval of teaching information that does not incorporate traditional music culture is in the interval of [0.25,0.35). This study contributes to the integration of traditional music culture and music teaching to a certain extent and enhances the effect of music teaching in colleges and universities.