Music Segments Research Articles

Pathos in the structure of Laz Ekwueme’s Choral Music: Hombe

Studies have covered analysis in Western and African compositions to include a detailed description of the arrangement of every segment of the music. However, less structural analysis has ventured into the pathos of the arrangement of the musical segments. Engaging in the pathos of the structure of Laz Ekwueme’s choral music (Hombe) will bring to limelight how musical structure enhances emotions and psychological feelings. Methodology relies on analysing the musical elements explored in the composition and close score reading of Hombe using structural analysis model. The result shows that Ekwueme employed the Western four-part chorus arrangement as an ornamental device to establishing the diverse African music elements such as repetition of thematic idea in all parts for emphasis, syncopation, varied tempo and shifting accents and tonality thereby enhancing varied moments of emotions, affects and excitement. The paper concludes that the arrangement of different segments of Hombe created moments of extraordinary pathos evoking different emotions and psychological feelings. Musical structure is thus a compositional device with the power to evoke feelings thereby creating a characteristic pattern of a musical style.

Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses

Music is not only a worldwide essential cultural industry but also one of the most dynamic. The increasing volume of complex music-related data defines new challenges and opportunities for extracting knowledge, benefiting not only different music segments but also the Music Information Retrieval research field. In this article, we assess musical success in the United States and Brazil, two of the biggest music markets in the world. We first introduce MUHSIC and MUHSIC-BR, two novel datasets with enhanced success information that combine chart-related data with acoustic metadata to describe the temporal evolution of musical careers. Then, we use such enriched and curated data to cluster artists according to their success level by considering their high-impact periods (hot streaks). Our results reveal three groups with distinct success behavior over time. Furthermore, Brazil and the US present specific music success patterns regarding artists and genres, reflecting the importance of analyzing regional markets individually.

Collegiate musicians’ experiences with democratic rehearsal procedures

The purpose of this qualitative study was to explore collegiate musicians’ lived experiences with democratic rehearsal procedures in a concert band setting. A constructivist worldview and phenomenological framework were used to examine participants’ experiences using their own words through weekly written reflections, pre- and post- questionnaires, rehearsal interactions, and semi-structured interviews. Participants ( N = 42) were members of a co-directed, non-auditioned university concert band at a large midwestern university. Data collection took place during an eight-week concert cycle which included scaffolded democratic experiences such as (a) selecting music for performance, (b) participating in collaborative score study/preparation during rehearsal, (c) identifying and selecting music segments for rehearsal, (d) identifying and describing opportunities for individual and ensemble performance improvement, (e) analyzing, discussing, and making decisions regarding how the music should be performed, (f) participating in student-led sectionals, and (g) participating in student-led full ensemble rehearsal. Themes associated with participants’ experiences included (a) the value of multiple perspectives, (b) ownership and musical agency, (c) engagement, and (d) providing feedback. Participants indicated that the democratic rehearsal cycle was generally a positive experience. Application of democratic rehearsal procedures may aid students in developing their agency, musicianship, and musical independence.

Аctivity of Сhinese symphony orchestras in the enrichment of the modern socio-cultural space of the PRC

The purpose of the article is to identify the factors of influence of Chinese symphony orchestras on the enrichment of the socio-cultural space of the country. An important task is to characterize the activities, which provide artistic events and saturation of the cultural landscape with new layers of content.
 The methodology is based on the application of systemic and socio-cultural approaches; the event analysis method and the descriptive method are applied too.
 The results of the study. China’s socio-cultural space has undergone significant changes in the last few decades, and its structure is complex and multidimensional. It can be stated that the function of the determining system-forming factor of its unity can be performed by culture.
 One of the conditions for the successful functioning and development of national musical culture in its musical segment is the development of the most complex genre areas — symphony and opera. Symphony orchestras, an attribute of modern music culture, currently work in the biggest cities of the country. During the policy of reforms and openness, radical changes took place in the activities of orchestras. Among other things, an important mission of the orchestras was to perform works by Chinese composers who are actively mastering the field of music, which is relatively new to the national culture.
 According to the study’s analysis of various aspects of symphony orchestras in modern China, these creative groups are actively involved not only in the musical and cultural process, but also in the socio-cultural process as a whole. Their contribution to the renewal and enrichment of China’s cultural landscape is made both at the event level and at the substantive level.
 The repertoire policy of orchestras, which is implemented in two directions — involving listeners in masterpieces of world music and promoting the work of the most talented domestic composers — ensures the diversity of the sound environment of the country and stimulates various forms of intercultural communication. In addition to their natural professional activities, which have a strong influence on society, Chinese orchestras often resort to effective artistic actions, delivering unique messages to society on issues relevant to it: environmental protection, historical memory, values of national traditions and more. Transferring the reaction to socially significant events into the plane of creative rethinking, which has become widespread in modern Chinese culture, strengthens the unity of various elements of socio-cultural space.
 The scientific novelty is determined by the analysis of the activities of Chinese symphony orchestras in view of their integration into the socio-cultural environment of the country. A significant amount of new information has been introduced into scientific circulation.
 The practical significance. The results of the study can be used in scientific work in the field of culturology, as well as taken into account in the further planning of creative projects and forms of activities of orchestras, both in China and in other countries.

Timing-Sound Interactions

This article reports on a study of concepts and practices of microrhythm among skilled performers of traditional Scandinavian fiddle music, particularly the so-called springar tradition which features non-isochronous and variable beats and subdivisions within a triple meter framework. In this context, microrhythm refers to the overall shaping of musical events at the micro level, encompassing both timing (temporal placement and duration) and sound (shape/envelope, timbre and intensity). A particular focus is to explore how these musical features interact – as opposed to treating timing and sound separately – and how timing-sound interactions in turn are understood in terms of groove-forming elements. The study consisted of semi-structured interviews with four expert musicians, which have been transcribed and coded for key themes and concepts. Subsequently, these data were complemented by analyses of relevant musical segments. The interviews focused on the defining features of a good groove, and how aspects of sound are envisaged to affect aspects of rhythm and timing, and vice versa. It was found that groove is largely conceptualised in terms of movement and embodiment; that musical features (timing, accentuation, ornamentation, tone production) are seen to interact and overlap, suggesting a multi-parametrical concept of groove; and that melodic-rhythmic variation is an important groove-forming element. The interviews also revealed a strongly practice-oriented perspective on performance and aesthetics in which musical concepts are tightly integrated with their associated sound-producing movements (bowing, fingering, foot tapping).

Music emotion recognition based on segment-level two-stage learning

In most Music Emotion Recognition (MER) tasks, researchers tend to use supervised learning models based on music features and corresponding annotation. However, few researchers have considered applying unsupervised learning approaches to labeled data except for feature representation. In this paper, we propose a segment-based two-stage model combining unsupervised learning and supervised learning. In the first stage, we split each music excerpt into contiguous segments and then utilize an autoencoder to generate segment-level feature representation. In the second stage, we feed these time-series music segments to a bidirectional long short-term memory deep learning model to achieve the final music emotion classification. Compared with the whole music excerpts, segments as model inputs could be the proper granularity for model training and augment the scale of training samples to reduce the risk of overfitting during deep learning. Apart from that, we also apply frequency and time masking to segment-level inputs in the unsupervised learning part to enhance training performance. We evaluate our model on two datasets. The results show that our model outperforms state-of-the-art models, some of which even use multimodal architectures. And the performance comparison also evidences the effectiveness of audio segmentation and the autoencoder with masking in an unsupervised way.

Steerable Music Generation which Satisfies Long-Range Dependency Constraints

Although music is full of repetitive motifs and themes, artificially intelligent temporal sequence models have yet to demonstrate the ability to model or generate musical compositions that satisfy steerable, long-range constraints needed to evoke such repetitions. Markovian approaches inherently assume a strictly limited range of memory while neural approaches—despite recent advances in evoking long-range dependencies—remain largely unsteerable. More recent models have been published that attempt to evoke repetitive motifs by imposing unary constraints at intervals or by collating copies of musical segments. Although the results of these methods satisfy long-range dependencies, they come with significant—potentially prohibitive—sacrifices in the musical coherence of the generated composition or in the breadth of satisfying compositions which the model can create. We present R<small>EGUALR</small> non-homogeneous Markov models as a solution to the long-range dependency problem which uses R<small>ELATIONAL</small> automata to enforce binary constraints to compose music with repeating motifs. The solution we present preserves musical coherence (i.e., Markovian constraints) for the duration of the generated compositions and significantly increases the range of satisfying compositions that can be generated.

Data-Driven Computer Choreography Based on Kinect and 3D Technology

As a form of artistic expression, dance accompanied by music enriches the cultural life of human beings and stimulates the creative enthusiasm of the public. Choreography is usually done by professional choreographers. It is highly professional and time-consuming. The development of technology is changing the way of artistic creation. The development of motion capture technology and artificial intelligence makes computer-based automatic choreography possible. This paper proposes a method of music choreography based on deep learning. First, we use Kinect to extract and filter actions and get actions with high authenticity and continuity. Then, based on the constant Q transformation, the overall note density and beats per minute (BPM) of the target music are extracted, and preliminary matching is performed with features such as action speed and spatiality, and then, the local features of the music and action segments based on rhythm and intensity are matched. The experimental results show that the method proposed in this paper can effectively synthesize dance movements. The speed and other characteristics of each movement segment in the synthesis result are very uniform, and the overall choreography is more aesthetic.

GENRE ORIGINS AND WORLDVIEWS OF THE GALICIAN SPIWOHRA

Galician spiwohra is one of the genres that needs more extensive research in the reconstruction of the holistic panoramic image of the Ukrainian musical and theatrical heritage. Silenced throughout the Soviet period through the authorship of priests - composers and playwrights, the spiwohra needs both a more detailed study in its inseparable connection with various artistic sources, and clarifying the conditions of its return to the modern theater stage. The aim of the article is to find out the main national origins and Western European prototypes of the spiwohra of composerspriests of the second half of the XIX century. and its focus on the educational function among Ukrainians in Galicia The research methodology is reflected in the following tasks: 1) analysis of basic research on the work of composerspriests and the spiwohra in Ukrainian musicology; 2) distinguishing the main national and Western European prototypes of the Ukrainian Galician spiwohra; 3) substantiation of ethical and didactic orientation of the spiwohra; 4) generalization and disclosure of prospects for further research on this topic. Scientific novelty. The article systematizes the origins of the musical-scenic genre of spiwohra, and traces the ways and means of achieving various goals by composers-priests in this genre: the assertion of their national identity; representation of one's own spiritual tradition before representatives of other nations, establishment of cultural and artistic ties between neighboring nations Conclusion. Composers-priests in the spiwohra consistently nurtured the direction of performing arts, the importance of which for national culture is difficult to overestimate and which in modern conditions has not lost its relevance. His influence, despite all the avant-garde and postmodern modifications of modern theater, remains significant in the search for national priorities of performing arts. Undoubtedly, the musical segment of the Galician choir has received an independent artistic "biography" for many decades. Both the folk song, naturally included in the plot of the dramatic action, and the music, which was specially written for individual performances by composers-priests, if considered holistically, systematically, constitute a very voluminous layer of Ukrainian culture.

Self-Psychological Control and Creativity of Music Creation in Multimusic Environment.

This study aimed to solve the difficulties in the research of self-psychology, three executive controls, and creativity in multimusic environment. In today's teaching and performance, the study of piano adaptation has practical significance in broadening artistic vision, improving performance technology, and training symphonic music thinking. A research method of self-psychological three executive controls and creativity generation for creation in multimusic environment is proposed. A computer composition algorithm is based on the hidden Markov model and interactive genetic algorithm. By integrating melody elements and rhythm into the traditional Markov model with only notes or rhythm as the unit, a new hidden Markov music prediction model is established, and the interactive genetic algorithm is used to optimize the music segments. Simulation results show that the algorithm can use a small dataset to quickly create music clips with certain melody logic and in line with users' personality. It is proved that the method based on the hidden Markov model and interactive genetic algorithm can meet the research needs of self-psychological three execution control and creativity generation in multimusic environment.

Generic Symbolic Music Labeling Pipeline

The availability of large datasets is an essential key factor for machine learning success. However, for symbolic music datasets, while there are many symbolic music files available, labeled datasets are scarce in many applications. In this paper, we propose a general pipeline for symbolic music labeling. The input to the pipeline is unlabeled midi files without particular constraints. Firstly, the pipeline filters the input and splits it into time-limited musical segments. Secondly, the pipeline generates intermediate labels using multiple pre-trained models, neural networks, and heuristics. Finally, multiple methods are used to combine intermediate labels to generate final labels. A label is accepted only if it exceeds a certain confidence level. To test the pipeline, we apply it to label a new piano difficulty dataset, "PianoDiff." We provide a thorough analysis to facilitate its usage in piano difficulty estimation for classification and generation using machine learning approaches. We test our pipeline on a dataset with manual labels. A random forest model trained on the weakly labeled dataset achieves an F1 score with a relative improvement of 13 percentage points compared to the same model trained on a smaller manually labeled dataset.

CVAE-GAN Emotional AI Music System for Car Driving Safety

Musical emotion is important for the listener’s cognition. A smooth emotional expression generated through listening to music makes driving a car safer. Music has become more diverse and prolific with rapid technological developments. However, the cost of music production remains very high. At present, because the cost of music creation and the playing copyright are still very expensive, the music that needs to be listened to while driving can be executed by the way of automated composition of AI to achieve the purpose of driving safety and convenience. To address this problem, automated AI music composition has gradually gained attention in recent years. This study aims to establish an automated composition system that integrates music, emotion, and machine learning. The proposed system takes a music database with emotional tags as input, and deep learning trains the conditional variational autoencode generative adversarial network model as a framework to produce musical segments corresponding to the specified emotions. The system takes the music database with emotional tags as input, and deep learning trains the CVAE-GAN model as the framework to produce the music segments corresponding to the specified emotions. Participants listen to the results of the system and judge whether the music corresponds to their original emotion.

Musical and theatrical reflection of the creative practice of N.N. Sinelnikov: a cultural dimension

The purpose of the article is to carry out a culturological dimension and define as a synthetic phenomenon the musical-theatrical aspect of the artistic activity of the outstanding actor, director, theater teacher, and entrepreneur Mykola Mykolayovych Sinelnikov (late 19th – early 20th century). The methodology of research is based on the use of a complex of general scientific and specific scientific methods in accordance with the cultural discourse. In order to study the problem in a historical sequence, the historical method was applied. Setting the goal of illumination and analytical measurement of the constituent components of the problem under study, the system-analytical method is applied. The system-structural method is used to study a cultural phenomenon as a system of interrelated and interacting elements. The hermeneutic method is used to study and interpret the theoretical provisions of the problem under study. In order to study the life path of a creative person, illuminate her inner world, and understand the motives of her activity, the biographical method is used. The generalization method was applied to determine the general conclusions of the study. The scientific novelty lies in the musical and theatrical reflection of the creative phenomenon of the outstanding artist-reformer of the late 19th – early 20th centuries Mykola Sinelnikov. Conclusions. Consideration of the issue raised in the article made it possible to state the importance of the presentation of the creative practice of N.N. Sinelnikov as a representative of musical and theatrical syncretism. Research of the phenomenon of N.N. Sinelnikov made it possible to illuminate the system of artistic principles of the creator, which fully corresponded to the nature and content of the modern era. It was determined that the musical and theatrical segments of N.N. Sinelnikov’s works are harmonious integrity, that is, they represent musical and theatrical syncretism.&#x0D; Keywords: musical and theatrical syncretism, a creative phenomenon, syncretism, cultural studies, theater, music, musical and theatrical reflection, socio-cultural space.

Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure

To improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term correlation analysis. Then, the pitch frequency is extracted, and the music fragments are roughly classified by wavelet transform to realize the preprocessing of the music fragments. In order to calculate the confidence measure of the music segment, the SVM method is used, whereas the adaptive update of the confidence measure is studied using reliable data selection algorithm. The dynamic threshold notes are segmented according to the update result to realize music segmentation. Experimental results show that the recall and precision values of the algorithm reach 97.5% and 93.8%, respectively, the segmentation error rate is low, and it can achieve effective segmentation of music fragments, indicating that the algorithm is effective.

Classical Music Specific Mood Automatic Recognition Model Proposal

The purpose of this study was to propose an effective model for recognizing the detailed mood of classical music. First, in this study, the subject classical music was segmented via MFCC analysis by tone, which is one of the acoustic features. Short segments of 5 s or under, which are not easy to use in mood recognition or service, were merged with the preceding or rear segment using an algorithm. In addition, 18 adjective classes that can be used as representative moods of classical music were defined. Finally, after analyzing 19 kinds of acoustic features of classical music segments using XGBoost, a model was proposed that can automatically recognize the music mood through learning. The XGBoost algorithm that is proposed in this study, which uses the automatic music segmentation method according to the characteristics of tone and mood using acoustic features, was evaluated and shown to improve the performance of mood recognition. The result of this study will be used as a basis for the production of an affect convergence platform service where the mood is fused with similar visual media when listening to classical music by recognizing the mood of the detailed section.

Musical components important for the Mozart K448 effect in epilepsy

There is growing evidence for the efficacy of music, specifically Mozart’s Sonata for Two Pianos in D Major (K448), at reducing ictal and interictal epileptiform activity. Nonetheless, little is known about the mechanism underlying this beneficial “Mozart K448 effect” for persons with epilepsy. Here, we measured the influence that K448 had on intracranial interictal epileptiform discharges (IEDs) in sixteen subjects undergoing intracranial monitoring for refractory focal epilepsy. We found reduced IEDs during the original version of K448 after at least 30-s of exposure. Nonsignificant IED rate reductions were witnessed in all brain regions apart from the bilateral frontal cortices, where we observed increased frontal theta power during transitions from prolonged musical segments. All other presented musical stimuli were associated with nonsignificant IED alterations. These results suggest that the “Mozart K448 effect” is dependent on the duration of exposure and may preferentially modulate activity in frontal emotional networks, providing insight into the mechanism underlying this response. Our findings encourage the continued evaluation of Mozart’s K448 as a noninvasive, non-pharmacological intervention for refractory epilepsy.

Music emotion recognition using recurrent neural networks and pretrained models

The article presents conducted experiments using recurrent neural networks for emotion detection in musical segments. Trained regression models were used to predict the continuous values of emotions on the axes of Russell’s circumplex model. A process of audio feature extraction and creating sequential data for learning networks with long short-term memory (LSTM) units is presented. Models were implemented using the WekaDeeplearning4j package and a number of experiments were carried out with data with different sets of features and varying segmentation. The usefulness of dividing the data into sequences as well as the point of using recurrent networks to recognize emotions in music, the results of which have even exceeded the SVM algorithm for regression, were demonstrated. The author analyzed the effect of the network structure and the set of used features on the results of the regressors recognizing values on two axes of the emotion model: arousal and valence. Finally, the use of a pretrained model for processing audio features and training a recurrent network with new sequences of features is presented.

Research on Intelligent Recognition Method of Music Similar Segments Based on Deep Reinforcement Learning

The identification of similar music segments is of great significance for the study of online music search, content relevance, emotional expression and many other aspects. In the overall structure of music, the extraction of key frames, the identification of similar key frames for different types of music, which is to obtain better music emotion data. This paper uses in-depth reinforcement learning algorithms to analyze the music data in detail to construct music similarity Intelligently identify the database and match the obtained music files with the music data in the database to find similar segments. Case analysis shows that this method can effectively analyze music fragments and provide a basis for subsequent music control.

Research on Emotional Music Reconstruction Method Based on DBN-GRU

In the process of music therapy, in order to solve the problems of short music segments with poor emotional adjustment effect and poor emotional perception of patients, this paper proposes an emotional music reconstruction method that combines Deep Belief Networks (DBN) and Gated Recurrent Unit (GRU). According to the emotional music clips fed back by EEG, the high-dimensional features of music were extracted from the DBN network, and the music features were input into the GRU to construct the emotional music reconstruction model. The accuracy of note prediction of reconstructed music is analyzed, and the results show that this method is better than the reconstruction method using GRU alone. An anxiety evaluation experiment of the subjects was designed, and the Self-rating Anxiety Scale (SAS) of the subjects before and after playing reconstructed music was analyzed, thus verifying that the music generated by the emotional music reconstruction method can effectively alleviate anxiety, which is an excellent tool in the field of music therapy.

Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

With the rapid growth of digital music today, due to the complexity of the music itself, the ambiguity of the definition of music category, and the limited understanding of the characteristics of human auditory perception, the research on topics related to automatic segmentation of music is still in its infancy, while automatic music is still in its infancy. Segmentation is a prerequisite for fast and effective retrieval of music resources, and its potential application needs are huge. Therefore, topics related to automatic music segmentation have important research value. This paper studies an improved algorithm based on negative entropy maximization for well-posed speech and music separation. Aiming at the problem that the separation performance of the negative entropy maximization method depends on the selection of the initial matrix, the Newton downhill method is used instead of the Newton iteration method as the optimization algorithm to find the optimal matrix. By changing the descending factor, the objective function shows a downward trend, and the dependence of the algorithm on the initial value is reduced. The simulation experimental results show that the algorithm can separate the source signal well under different initial values. The average iteration time of the improved algorithm is reduced by 26.2%, the number of iterations is reduced by 69.4%, and the iteration time and the number of iterations are both small. Fluctuations within the range better solve the problem of sensitivity to the initial value. Experiments have proved that the new objective function can significantly improve the separation performance of neural networks. Compared with the existing music separation methods, the method in this paper shows excellent performance in both accompaniment and singing in separated music.