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In vocal singing, the combination of emotional expression and singing skills is an important part of music performance, emotional expression can be fully demonstrated through singing skills, at the same time, the realisation of singing skills can also promote the enhancement of the level of emotional expression in vocal singing, and the organic combination of the two in vocal singing has an important promotional value in promoting the enhancement of the singing effect. Therefore, this paper discusses the relationship between emotional expression and singing skills in vocal singing, analyses the combination strategy of emotional expression and singing skills in vocal singing, and on this basis further researches the enhancement of emotional expression and singing skills in vocal singing, aiming to provide effective guidance for vocal learners and help them to achieve the perfect integration of emotion and skills in singing. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology, the relationship between vocal singing training methods and big data technology is getting closer and closer. The application of training methods is a means of expressing vocal compositions. Emotional expression is the ultimate purpose and meaning of method optimization. Training methods and emotional expression are equally important in vocal singing, and both are indispensable. In this paper, a VSSA-separation model is established based on the Vocal Signal Separation Algorithm (VSSA). Focus on optimizing the training method of vocal singing. Through in-depth discussion of existing works, I try to find its inner influence and outer connection. In order to better understand the grasp and application of vocal singing method and emotional expression balance. It can be seen from the calculation that the image shaping in the song language accounts for 83% of the optimization of the training method. Inverse word processing in song language has a different proportion of the optimization of the training method. It accounts for 71% of English operas. It accounts for only 13% of Chinese operas. Artistic effects have the highest average optimization difficulty among the training methods. Its optimization difficulty is as high as 89%.

In this paper, starting from the fusion of audio-visual multisensory vocal music feature representation, the RAE algorithm is used to represent the music lyrics text in vocal singing teaching. Due to the heterogeneity of the feature space of the audio modality and the lyrics modality, which makes it exceptionally tricky to directly mine the correlation between these two modalities, it is necessary to optimize the research through the fusion of the audio and the textual modality for the representation of the implicit space of the music. According to the SVM’s optimal classification, hyperplane is not limited by the data dimension. Combined with the kernel function parameters and emotional LFSM fusion, a visual multisensory vocal singing teaching emotion model based on SVM is constructed. The parameter environment and dataset are selected, and the comparison method and evaluation criteria are determined to analyze emotional research on vocal singing teaching in colleges and universities. The results show that in terms of model performance, the SVM model in this paper is 8.5% higher than model 6, reaching the highest 0.873, with stronger emotion extraction and recognition ability, greatly improving the emotion classification results of the model. The multimedia type of audio singing material is the least helpful for expression and physical performance in vocal singing teaching, with a total value of 67. This study provides a more comprehensive understanding of the emotional changes of students in teaching activities so as to identify problems, improve and optimize them, and give more students guidance and support in performing vocal singing.

In this paper, the extracted acoustic features are processed using the MFCC method to classify the sound recognition. The linear spectrum of the sound is mapped onto the Mel FPP coefficients and Mel nonlinear spectrum, respectively, and the MFCC is obtained by cepstrum processing. The Mel FPP coefficients and Gaussian mixture model are combined to create the acoustic model. On this basis, the art and emotional skills of vocal singing are explored, and simulation and empirical experiments are set up to analyze the effect of the model constructed in this paper. The experimental results show that the model constructed in this paper tends to stabilize when the number of iterations is 2, the final accuracy rate stabilizes near 0.9, and the model is effective. Different vocal types are recognized by the model constructed in this paper, and the recognition rate of emotional expression of all vocal types is above 0.7, and the model recognition rate is high. The accuracy and recall classification for emotional expression techniques are 0.73 and 0.79, resulting in a more balanced evaluation overall.

The continuous progress of artificial intelligence technology has shown great potential for application in several fields, especially music. The research direction of Objective Evaluation of Vocal Singing Effectiveness uses advanced technologies to analyze and assess a singer’s performance across multiple dimensions, including pitch, rhythm, and timbre, and is highly valuable. Building an accurate and fair evaluation system faces many challenges, including how to accurately capture and analyze the subtle changes in the voice and synthesize the effects of different musical elements on the quality of the performance. This requires researchers to explore music theory, sound analysis techniques, and artificial intelligence algorithms, and develop a new methodology that can comprehensively evaluate the effectiveness of vocal singing. This paper constructs a complete set of vocal singing evaluation models by analyzing acoustic feature extraction, Hidden Markov Model, and Generalized Regression Radial Basis Function Network in detail. The study adopts a logarithmic Mel spectrum for acoustic feature extraction to effectively capture the essential attributes of the singing voice. Hidden Markov models and mixed Gaussian models are used to model the sound signal, improving phoneme recognition accuracy. Accurate singing effect was evaluated using a generalized regression radial basis function network. In this article, the accuracy of this evaluation method in terms of pitch, rhythm, and timbre reached 95%, 93%, and 89%, respectively, demonstrating high evaluation consistency and reliability. The research method provides a new objective evaluation tool for vocal singing effects, which is valuable for vocal teaching and self-practice.

The purpose of the work is to know the elements related to the appearance of choral music, the sources of inspiration, the origin, the procedures used and the way it has evolved over time, up to the present day. The work aims at an announced knowledge of the evolution of choral music, with the goal of keeping alive the sources of folkloric, church inspiration, and last but not least, the composers who contributed to the development of Romanian music in a European context. An evolutionary path of Romanian choral music supports and helps to develop importance, a field that was born and evolved over time with the love and implications of composers, after the 20th century and up to the present day. [ABSTRACT FROM AUTHOR]

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Abstracts: Objective: The purpose of the present study was to investigate vocal singing performance of hearing-impaired children with cochlear implants (CI) and hearing aids (HA) as well as to evaluate the relationship between demographic factors of those hearing-impaired children and their singing ability. Methods: Thirty-seven prelingually-deafened children with CIs and 31 prelingually-deafened children with HAs, and 37 normal-hearing (NH) children participated in the study. The fundamental frequencies (F0) of each note in the recorded songs were extracted and the duration of each sung note was measured. Five metrics were used to evaluate the pitch-related and rhythm-based aspects of singing accuracy. Results: Children with CIs and HAs showed significantly poorer performance in either the pitch-based assessments or the rhythm-based measure than the NH children. No significant differences were seen between the CI and HA groups in all of these measures except for the mean deviation of the pitch intervals. For both hearing-impaired groups, length of device use was significantly correlated with singing accuracy. Conclusions: There is a marked deficit in vocal singing ability either in pitch or rhythm accuracy in a majority of prelingually-deafened children who have received CIs or fitted with HAs. Although an increased length of device use might facilitate singing performance to some extent, the chance for the hearing-impaired children fitted with either HAs or CIs to reach high proficiency in singing is quite slim. [Copyright &y& Elsevier]

Abstract: The coarse pitch information in cochlear implants might hinder the development of singing in prelingually-deafened pediatric users. In the present study, seven prelingually-deafened children with cochlear implants (5.4–12.3years old) sang one song that was the most familiar to him or her. The control group consisted of 14 normal-hearing children (4.1–8.0years old). The fundamental frequencies (F0) of each note in the recorded songs were extracted. The following five metrics were computed based on the reference music scores: (1) F0 contour direction of the adjacent notes, (2) F0 compression ratio of the entire song, (3) mean deviation of the normalized F0 across the notes, (4) mean deviation of the pitch intervals, and (5) standard deviation of the note duration differences. Children with cochlear implants showed significantly poorer performance in the pitch-based assessments than the normal-hearing children. No significant differences were seen between the two groups in the rhythm-based measure. Prelingually-deafened children with cochlear implants have significant deficits in singing due to their inability to manipulate pitch in the correct directions and to produce accurate pitch height. Future studies with a large sample size are warranted in order to account for the large variability in singing performance. [Copyright &y& Elsevier]