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Background: Few studies have found long-term effects of early musical environmental enrichment in the NICU on preterm infant's development. This study examines how early music enrichment affects emotional development and effortful control abilities in 12- and 24-month-old very preterm (VPT) infants. Methods: One hundred nineteen newborns were recruited, including 83 VPTs and 36 full-term (FT) infants. The VPT infants were randomly assigned to the music intervention (44 VPT-Music) or control (39 VPT-control) groups. VPTMusic infants listened specifically designed music intervention from the 33rd week of gestation until hospital discharge. At 12 and 24 months, children were clinically evaluated using the Bayley-III Scales of Infant and Toddler Development and the Laboratory Temperament Assessment Battery, and at 24 months, with 3 additional episodes of the Effortful Control Battery. Results and discussion: Our analysis showed that during a fear eliciting task, the VPT-Music group expressed lower level of fear reactivity and higher positive motor actions than VPT-controls and FT infants. At 24 months, the VPT-music group had lower scores for negative motor actions in the joy task, compared to both VPT-control and FT groups. In addition, both FT and VPT-music had higher scores of sustained attention compared to VPT-controls, but the contrasts were not significant. No significant effects on mental, language and motor outcomes were identified and for all three dimensions of the ECBQ. Conclusion: The present study suggests that an early music intervention in the NICU might influence preterm children's emotional processing at 12 and 24 months. Limitations and suggestions for future research are highlighted. [ABSTRACT FROM AUTHOR]

In the realm of artificial intelligence and musicology, emotion recognition in music performance has emerged as a pivotal area of research. This paper introduces EmoTrackNet, an integrated deep learning framework that combines sparse attention networks, enhanced one-dimensional residual Convolutional Neural Networks (CNNs) with an improved Inception module, and Gate Recurrent Units (GRUs). The synergy of these technologies aims to decode complex emotional cues embedded in music. Our methodology revolves around leveraging the sparse attention network to efficiently process temporal sequences, thereby capturing the intricate dynamics of musical pieces. The incorporation of the 1D residual CNN with an upgraded Inception module facilitates the extraction of nuanced features from audio signals, encompassing a broad spectrum of musical tones and textures. The GRU component further refines the model's capability to retain and process sequential information over longer timeframes, essential for understanding evolving emotional expressions in music. We evaluated EmoTrackNet on the Soundtrack dataset a comprehensive collection of music pieces annotated with emotional labels. The results demonstrated remarkable improvements in the accuracy of emotion recognition, outperforming existing models. This enhanced performance can be attributed to the integrated approach, which efficiently combines the strengths of each component, leading to a more robust and sensitive emotion detection system. EmoTrackNet's novel architecture and promising results pave the way for new avenues in musicology, particularly in understanding and interpreting the emotional depth of musical performances. This framework not only contributes significantly to the field of music emotion recognition but also has potential applications in music therapy, entertainment, and interactive media where emotional engagement is key. [ABSTRACT FROM AUTHOR]

The relation between emotions and music is substantial because music as an art can evoke emotions. Music emotion recognition (MER) studies the emotions that music brings in the effort to map musical features to the affective dimensions. This study conceptualizes the mapping of music and emotion as a multivariate time series regression problem, with the aim of capturing the emotion flow in the Arousal-Valence emotional space. The Efficient Net-Music Informer (ENMI) Network was introduced to address this phenomenon. The ENMI was used to extract Mel-spectrogram features, complementing the time series data. Moreover, the Music Informer model was adopted to train on both time series music features and Mel-spectrogram features to predict emotional sequences. In our regression task, the model achieved a root mean square error (RMSE) of 0.0440 and 0.0352 in the arousal and valence dimensions, respectively, in the DEAM dataset. A comprehensive analysis of the effects of different hyperparameters tuning was conducted. Furthermore, different sequence lengths were predicted for the regression accuracy of the ENMI Network on three different datasets, namely the DEAM dataset, the Emomusic dataset, and the augmented Emomusic dataset. Additionally, a feature ablation on the Mel-spectrogram features and an analysis of the importance of the various musical features in the regression results were performed, establishing the effectiveness of the model presented herein. [ABSTRACT FROM AUTHOR]

This work focuses on an intelligent management framework for vocal music media resources, with use of deep neural networks and web crawlers. The web driver module is used to simulate the Google browser access mode to collect and preprocess the data of the China vocal music network webpage project, and the delay code is added to solve the problem of Internet Protocol (IP) restrictions on crawling webpages. After parsing, the content of the project description is obtained and temporarily stored in the local file through the panda’s module to realize the automatic collection of project data. Membership functions of four basic types of emotions are designed. By utilizing the analytic hierarchy process, the experience and wisdom of many music experts are gathered. The weights of activity and the evoking force in determining the music emotion membership value are discussed, trying to make the membership function closer to reality. Through the application of the music emotion analysis model, the music emotion analysis technology is combined with the recommendation algorithm, and the music recommendation algorithm combined with the emotion analysis is realized. The experimental test proves that the algorithm has higher accuracy than the traditional music recommendation algorithm. [ABSTRACT FROM AUTHOR]

In order to perform emotion prediction in music quickly and accurately, we have proposed a muscular and tall neural network architecture for music emotion classification. Specifically, during the audio pre-processing stage, we converge mel-scale frequency cepstral coefficients features and residual phase features with weighting, enabling the extraction of more comprehensive music emotion characteristics. Additionally, to enhance the accuracy of predicting musical emotion while reducing computational complexity during training phase, we consolidate Long short term memory network with Broad learning system network. We employ long short term memory structure as the feature mapping node of broad learning system structure, leveraging the advantages of both network models. This novel Neural Network architecture, called BLNN (Broad-Long Neural Network), achieves higher prediction accuracy. i.e., 66.78%, than single network models and other benchmark with/without consolidation methods. Moreover, it achieves lower time complexity than other excellent models, i.e., 169.32 s of training time and 507.69 ms of inference time, and achieves the optimal balance between efficiency and performance. In short, the extensive experimental results demonstrate that the proposed BLNN architecture effectively predicts music emotion, surpassing other models in terms of accuracy while reducing computational demands. In addition, the detailed description of the related work, along with an analysis of its advantages and disadvantages, and its future prospects, can serve as a valuable reference for future researchers. [ABSTRACT FROM AUTHOR]

Music listening is enjoyed across the lifespan and around the world. This has spurred many theories on the evolutionary purpose of music. The Music for Social Bonding hypothesis posits that the human capacity to make music evolved for the purpose of creating and preserving relationships between one another. Considering different time periods of music use across the lifespan, adolescence is especially a period of social reorientation away from family towards peers, characterized by new social bonds and increased prosocial behavior. This shift is accompanied by notable structural and functional changes in brain networks supporting reward processing and prosocial behavior. Reviewing the extant literature on developmental cognitive neuroscience and adolescent music use, we propose that neurocognitive changes in the reward system make adolescence an ideal developmental time window for investigating interactions between prosocial behavior and reward processing, as adolescence constitutes a time of relative increase in music reward valuation. Testing this hypothesis may clarify our understanding of developmental trajectories in music reward valuation, and offer insights into why music from adults' adolescence holds a great deal of personal significance. [ABSTRACT FROM AUTHOR]

The editorial in Teaching & Learning in Medicine delves into the idea of 'secret affinities' in medical education inquiry, urging educators to engage in philosophical exploration for a deeper understanding of educational outcomes. Drawing on philosopher Walter Benjamin's work on language and translation, the text challenges traditional interpretations and emphasizes the significance of experience expressed through language in medical education and practice. It discusses the role of translation in conveying meaning between languages, highlighting how humans act as translators in their experiences, emphasizing the importance of listening and attentiveness in understanding the world through translation. [Extracted from the article]

Music is a powerful language capable of eliciting a variety of emotions in individuals. Understanding and recognizing these emotions is pivotal for applications ranging from personalized music recommendations and music therapy to automatic music composition and affective computing. Presently, deep learning for music emotion recognition is gaining popularity, primarily relying on timbre features to capture local spatial information. However, there is an untapped potential in incorporating other pertinent audio features and global correlations in the feature space to capture the repetitive temporal information of music for emotion classification. This study introduces GlocalEmoNet as a method to capture both local and global correlations in music, utilizing timbre and Chroma audio features for tasks related to emotion classification and segmentation. The neural network underwent training and testing on approximately six thousand music audio samples, encompassing six music-emotion categories. The utilization of a genetic algorithm is employed for optimizing the hyperparameters of the proposed neural networks, aiming to attain optimal performance, efficiency, and generalization. The best classifier demonstrated superior performance, surpassing previously published results by a significant margin of approximately 14%. The optimal classifier achieved an accuracy score of 81.66%, an f1-score of 0.812, and an area under the curve score of 0.956. The evaluation of classification and segmentation outcomes also involved the use of visual representations. [ABSTRACT FROM AUTHOR]

Background/Objectives: Given the importance of emotion recognition for communication purposes, and the impairment for such skill in CI users despite impressive language performances, the aim of the present study was to investigate the neural correlates of emotion recognition skills, apart from language, in adult unilateral CI (UCI) users during a music in noise (happy/sad) recognition task. Furthermore, asymmetry was investigated through electroencephalographic (EEG) rhythm, given the traditional concept of hemispheric lateralization for emotional processing, and the intrinsic asymmetry due to the clinical UCI condition. Methods: Twenty adult UCI users and eight normal hearing (NH) controls were recruited. EEG gamma and alpha band power was assessed as there is evidence of a relationship between gamma and emotional response and between alpha asymmetry and tendency to approach or withdraw from stimuli. The TAS-20 questionnaire (alexithymia) was completed by the participants. Results: The results showed no effect of background noise, while supporting that gamma activity related to emotion processing shows alterations in the UCI group compared to the NH group, and that these alterations are also modulated by the etiology of deafness. In particular, relative higher gamma activity in the CI side corresponds to positive processes, correlated with higher emotion recognition abilities, whereas gamma activity in the non-CI side may be related to positive processes inversely correlated with alexithymia and also inversely correlated with age; a correlation between TAS-20 scores and age was found only in the NH group. Conclusions: EEG gamma activity appears to be fundamental to the processing of the emotional aspect of music and also to the psychocognitive emotion-related component in adults with CI. [ABSTRACT FROM AUTHOR]

With increasing interest in the emotional responses to music, some studies are specifically looking into music's regulatory function on emotion, known as "music emotion regulation (MER)". The purpose of this study was to examine the concepts on the regulatory impact of music on emotion using a scoping review procedure. Through an electronic database and manual searches based on the guidelines suggested by the JBI Manual for Evidence Synthesis, a total of 47 studies were identified and included for analysis. The results showed that there were some definitional challenges in each component of music emotion regulation. Most studies treated music as a single variable without exploring the regulatory mechanism of the intra-musical elements that constitute music. When examining the regulatory impact of music on emotion, emotion was inclusive of other terms ranging from feeling to mood. Most of the MER studies employed the terms used in the emotion regulation strategies; however, there were some regulatory processes that pertained solely to music's cathartic effect. Overall, the results showed that music emotion regulation (MER) was not clearly distinguished from music emotion (ME) studies. Future research should allocate more attention to the theoretical mechanism behind music's intrinsic regulatory effect in emotion regulation (MER) studies. [ABSTRACT FROM AUTHOR]

Music is an element that appears systematically as part of the rhetorical strategy of audiovisual narratives and is, to that extent, a constitutive part of them. Since audiovisual narratives, especially fictional ones, can be considered as poetic works in the Aristotelian sense, it is worth asking about the role that music plays in this poetic condition. In this research article, a theoretical proposal along these lines is put forward that will serve both to explain the role of music in the work of fiction and to analyze it. For the development of this proposal, the basis of the theory is based on the Aristotelian corpus (in particular, Poetics, Politics and Rhetoric), from which music can be understood as an expressive resource linked to the representation and creation of emotion. For this point, it is proposed to develop a theory of emotions, elaborated fundamentally from the studies of M. Nussbaum and M. Scheler, whose contributions connect the emotional processes with the recognition of ethical values. These ethical values, linked to emotions and their corresponding musical expressions, are to serve the purpose of discerning the function that music fulfills in the “letting oneself be followed” of history as a whole, by pointing out the prevailing emotional state or adjusted to the action. [ABSTRACT FROM AUTHOR]

Traditional music emotion recognition (MER) faces problems such as lack of contextual information, inaccurate recognition of music emotions, and difficulty in handling nonlinear relationships. This article first used long short-term memory (LSTM) networks to capture global information and contextual relationships of music. Subsequently, the DCNN was chosen to process sequence data and capture global dependencies to improve the accuracy of MER. Finally, a MER model was constructed based on DCNN to recognize and classify music emotions. This article obtained the impact of different parameter values on model training iterations by adjusting hyperparameters related to training. The optimal values for learning rate μ , momentum coefficient α , weight attenuation coefficient γ , and Dropout coefficient were 0.01, 0.7, 0.0003, and 0.5, respectively. The DCNN used in this article was iteratively trained with recurrent neural networks, convolutional recurrent neural networks, and transform domain neural networks for audio spectrograms, and the results were compared. The experimental findings indicated that the spectral recognition accuracy of DCNN was stable at 95.68%, far higher than the other three different networks. The results showed that the DCNN method used in this article could more accurately distinguish different negative emotions and positive emotions. [ABSTRACT FROM AUTHOR]

With the rapid development of music digitization and online streaming services, automatic analysis and classification of music content has become an urgent need. This research focuses on music sentiment analysis, which is the identification and classification of emotions expressed by music through algorithms. The study defines and classifies possible emotions in music. Then, advanced artificial intelligence techniques, including traditional machine learning and deep learning methods, were employed to perform sentiment analysis on music fragments. In the process of creating and validating the model, the combination of convolutional neural network and long term memory network shows excellent performance in various performance indicators. However, for some complex or culturally ambiguous music fragments, the model may also suffer from misclassification problems. This provides the direction for further optimization of future research aimed at achieving more accurate music emotion analysis. [ABSTRACT FROM AUTHOR]

Building on research demonstrating the benefits of music training for emotional prosody recognition in nontonal languages, this study delves into its unexplored influence on tonal languages. In tonal languages, the acoustic similarity between lexical tones and music, along with the dual role of pitch in conveying lexical and affective meanings, create a unique interplay. We evaluated 72 participants, half of whom had extensive instrumental music training, with the other half serving as demographically matched controls. All participants completed an online test consisting of 210 Chinese pseudosentences, each designed to express one of five emotions: happiness, sadness, fear, anger, or neutrality. Our robust statistical analyses, which included effect size estimates and Bayesian factors, revealed that music and nonmusic groups exhibit similar abilities in identifying the emotional prosody of various emotions. However, the music group attributed higher intensity ratings to emotional prosodies of happiness, fear, and anger compared to the nonmusic group. These findings suggest that while instrumental music training is not related to emotional prosody recognition, it does appear to be related to perceived emotional intensity. This dissociation between emotion recognition and intensity evaluation adds a new piece to the puzzle of the complex relationship between music training and emotion perception in tonal languages. [ABSTRACT FROM AUTHOR]

Music is an important part of life for many people. It can evoke a wide range of emotions, including sadness, happiness, anger, tension, relief and excitement. People with hearing loss and people with cochlear implants have reduced abilities to discriminate some of the features of musical sounds that may be involved in evoking emotions. This paper reviews these changes in perceptual abilities and describes how they affect the perception of emotion in music. For people with acquired partial hearing loss, it appears that the perception of emotion in music is almost normal, whereas congenital partial hearing loss is associated with impaired perception of music emotion. For people with cochlear implants, the ability to discriminate changes in fundamental frequency (associated with perceived pitch) is much worse than normal and musical harmony is hardly perceived. As a result, people with cochlear implants appear to judge emotion in music primarily using tempo and rhythm cues, and this limits the range of emotions that can be judged. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'. [ABSTRACT FROM AUTHOR]

The aim of the study was to explore whether there are differences in the effects of temporary and short-term interventions for test anxiety (TA) between aesthetic musical emotions and everyday positive emotions based on the mutual promotion and mutual counteraction (MPMC) theory of affect. In Study 1, one hundred and three valid participants with high TA (38 males, 23.29 ± 1.05 years old on average) were equally divided into three groups by matching, and different genres of audio materials (comedic audio, vital music, sublime music) were used to induce different types of emotions (everyday positive emotions, vital music emotions, sublime music emotions) in order to test the effect of temporary intervention on TA. In Study 2, a one-week short-term intervention was conducted based on the former study to further investigate the effects of different audio types in alleviating TA. The results showed an experimental dissociation from Study 1, demonstrating that sublime music had the best effect on sustained intervention of TA, followed by comedic audio, and the effect of vital music was slightly weaker than the previous two. Findings suggest that music genre may be an important factor that should not be overlooked when alleviating TA with music. [ABSTRACT FROM AUTHOR]

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method. [ABSTRACT FROM AUTHOR]

Artistic engagement and enjoyment are conceptualized as mechanisms for positively affecting well-being. However, less is known about how negative extramusical information influences enjoyment and/or engagement. We asked if knowing the immoral character of a composer and/or their compositional intent negatively affected listeners’ engagement and enjoyment. Participants (N = 448) read a fictionalized excerpt about Holst’s Mars, with either sexist or neutral composer information, and either sexist or neutral musical intent before listening to the work and completing measures of their enjoyment, engagement, and aesthetic response. We found an effect of extramusical information for listeners’ enjoyment and aesthetic response, but not for engagement. Enjoyment and aesthetic response were higher in the Neutral Composer/Neutral Intent condition than the other three conditions. This study furthers understanding of how extra-musical factors affect the positive emotions music elicits, and impact how listeners interact with music, thus impacting possibilities for enhanced senses of well-being. [ABSTRACT FROM AUTHOR]

Children with Autism Spectrum Disorder (ASD) show deficits in social interaction and social communication. There are some physical and psychological problems. Some children with autism spectrum disorder exhibit difficulties in expressing their emotions. However, emotions play a crucial role in social interaction and communication. Various music therapy methods have been developed to support emotional expression in this population, one of which is improvisational music therapy. This study was aimed to know the effectiveness of improvisational music therapy for children who were diagnosed with autism spectrum disorder in terms of emotion regulation. The design of this research is a quantitative method using randomised controlled trials (RCT). Pre- and post-tests were used in this study. The tool of this research is the Difficulties in Emotion Regulation Scale (DERS). The result of this RCT maps the effectiveness of improvisational music therapy for emotion regulation. Improvisational music therapy can be used by children to express their emotions freely. This study demonstrates that participants were able to regulate their emotions and exhibited significant positive changes in perception building. The findings suggest that improvisational music therapy can be a valuable intervention for other populations experiencing difficulties with emotional regulation and a means to find connections between the mind and the events in the real world. [ABSTRACT FROM AUTHOR]

Emotion terms such as “sad”, “happy”, and “joyful” apply to a wide range of entities. We use them to refer to mental states of sentient beings, and also to describe features of non‐mental things such as comportment, nature, events, artworks and so on. Drawing on the literature on polysemy, this article provides an in‐depth analysis of emotion descriptions. It argues that emotion terms are polysemous and distinguishes seven related senses. In addition, the article applies the analysis to shed light on a long‐standing debate in philosophy of music concerning emotion descriptions of music. [ABSTRACT FROM AUTHOR]

Background: The value of music lies in its ability to evoke emotions. People can gain emotional experiences in music and can also regulate their own emotions through music. Music has its own structural rules, and exploring the relationship between musical structure and emotions is an important approach to understanding the mechanism of music-induced emotions. Musical mode refers to the arrangement of intervals around the tonic, presenting different musical modes based on the central tone and the arrangement of intervals, including Chinese pentatonic modes and Western major and minor modes. Musical morphology indicates significant differences in the construction intensity of traditional Chinese pentatonic modes and major and minor modes, affecting their mode forms and thus determining their adaptability to external influences. Aims: Exploring the modalities of music and the effects of individual music training experiences on emotion induction; validating whether musical modes exhibit cross-cultural universality in the process of emotion induction. Method: This study recruited 65 university students as participants (34 with music training experience, 31 without music training experience). Through a passive listening paradigm using the GEMS and combined with a biofeedback equipment, it explored the differences in behavioral and physiological indicators (skin conductance, temperature, heart rate) of emotional experiences (basic and aesthetic emotions) influenced by the modal forms of Chinese traditional pentatonic modes and Western major and minor modes. Results: Firstly, the arousal level of music emotion is a primary factor influencing individuals' aesthetic emotional experiences in music, which is related to the intensity of modal construction in music; Secondly, the emotional pleasure and skin temperature change induced by pentatonic music are greater than those induced by major and minor modes; Thirdly, the arousal level, electrodermal change, and heart rate variability of major and minor modes are greater than those of pentatonic music; Finally, music training experience enhances college students' familiarity and preference for pentatonic music, thereby strengthening the electrodermal physiological indicators of emotional experiences. Conclusion: The different modal forms of music express different levels of emotional arousal, leading to differences in individuals' emotional dimensions and physiological indicators in music. Additionally, individuals' music training experiences and cultural backgrounds also influence their experience of music emotions. [ABSTRACT FROM AUTHOR]

A large body of research establishes the efficacy of musical intervention in many aspects of physical, cognitive, communication, social, and emotional rehabilitation. However, the underlying neural mechanisms for musical therapy remain elusive. This study aimed to investigate the potential neural correlates of musical therapy, focusing on the changes in the topology of emotion brain network. To this end, a Bayesian statistical approach and a cross-over experimental design were employed together with two resting-state magnetoencephalography (MEG) as controls. MEG recordings of 30 healthy subjects were acquired while listening to five auditory stimuli in random order. Two resting-state MEG recordings of each subject were obtained, one prior to the first stimulus (pre) and one after the final stimulus (post). Time series at the level of brain regions were estimated using depth-weighted minimum norm estimation (wMNE) source reconstruction method and the functional connectivity between these regions were computed. The resultant connectivity matrices were used to derive two topological network measures: transitivity and global efficiency which are important in gauging the functional segregation and integration of brain network respectively. The differences in these measures between pre- and post-stimuli resting MEG were set as the equivalence regions. We found that the network measures under all auditory stimuli were equivalent to the resting state network measures in all frequency bands, indicating that the topology of the functional brain network associated with emotional regulation in healthy subjects remains unchanged following these auditory stimuli. This suggests that changes in the emotion network topology may not be the underlying neural mechanism of musical therapy. Nonetheless, further studies are required to explore the neural mechanisms of musical interventions especially in the populations with neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between musical training and Emotional Intelligence (EI) among undergraduate and postgraduate students from diverse academic disciplines across six universities in India. The primary objectives are to assess EI levels in students trained and not trained in Indian Classical Music and to examine the influence of gender and type of musical training on EI. A sample of 130 students was divided into two groups: those with compulsory musical association for five years or more and those without musical exposure. Data was collected using a validated Emotional Intelligence Scale based on Goleman's model. The results indicate that students with musical training have significantly higher EI levels than their non-musical counterparts. Additionally, male students demonstrated higher EI levels than female students, while no significant difference was found between students learning instrumental and vocal music. These findings highlight the positive impact of musical training on emotional intelligence and suggest that music education could play a crucial role in enhancing students' emotional and social competencies. The study's implications extend to educational and developmental contexts, advocating for integrating music education compulsorily in education at every level. [ABSTRACT FROM AUTHOR]

In this study, we present a novel approach for music emotion recognition that utilizes a stacked ensemble of models integrating audio and lyric features within a structured song framework. Our methodology employs a sequence of six specialized base models, each designed to capture critical features from distinct song segments: verse1, chorus, and verse2. These models are integrated into a meta-learner, resulting in superior predictive performance, achieving an accuracy of 96.25%. A basic stacked ensemble model was also used in this study to independently run the audio and lyric features for each song segment. The six-input stacked ensemble model surpasses the capabilities of models analyzing song parts in isolation. The pronounced enhancement underscores the importance of a bimodal approach in capturing the full spectrum of musical emotions. Furthermore, our research not only opens new avenues for studying musical emotions but also provides a foundational framework for future investigations into the complex emotional aspects of music. [ABSTRACT FROM AUTHOR]

Enhancing user experience in music listening through facial emotion recognition presents a novel avenue for curating personalized song playlists, obviating the need for traditional search methodologies like keyword inputs or manual playlist exploration. This research pivots towards leveraging advanced pre-trained models, specifically ResNet18 and VGG19, for the accurate detection and analysis of listeners' emotions via facial expressions. By integrating these models with the Facial Emotion Recognition (FER) 2013, and The Extended Cohn-Kanade Dataset (CK+) dataset, our study aims to precisely identify a range of emotions including happiness, anger, sadness, surprise, disgust, and neutrality. In parallel, we employ Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) techniques to discern musical emotions from Spotify's dataset, encompassing moods such as happiness, sadness, energy, and calmness. A unique set of rules is formulated to amalgamate the insights from facial emotion recognition with musical emotion data, facilitating the automated suggestion of playlists that resonate with the user's current emotional state. Our experiments demonstrate notable results with the ResNet18 model achieved 72.67% accuracy and a 72.30% F1 score on the FER2013 dataset, with significant improvements on the CK+ dataset, reaching 96.97% accuracy and a 96.60% F1 score. The VGG19 model similarly excelled, particularly on the CK+ dataset with 95.96% accuracy and a 96.60% F1 score. On the FER2013 dataset, the VGG19 model achieved an accuracy of 72.64%, and an F1 score of 72.60%. Our music emotion recognition approach, using SVM on Spotify's dataset, achieved an 86.96% accuracy and an 84.54% F1 score. When using the LDA model on the same dataset, the accuracy was 79.00% and an F1 score of 76.72%. To bring this concept to fruition, we have developed an intuitive web application that allows users to capture their facial expressions via their device's camera, subsequently offering song recommendations tailored to their detected mood. [ABSTRACT FROM AUTHOR]

The research of music emotion recognition has broad application prospects in the fields of music intelligent recommendation and music visualization. Aiming at the problem that only using low‑level audio features for emotion recognition has limited effectiveness and poor interpretability. Firstly, an emotion recognition model ERMSLM based on MIDI (musical instrument digital interface) data is constructed, which can learn the semantic information of notes. The features of this model are composed of melodic features extracted with skip‑gram and LSTM (long short‑term memory), tonal features extracted by pre‑trained MLP and manually constructed features. Secondly, an emotion recognition model ERMBT based on text data that integrates lyrics and social tags is constructed. The lyrics features are composed of emotional features extracted with BERT, emotional dictionary features constructed by using ANEW lists and TF-IDF features of lyrics. Finally, two multimodal fusion models of feature‑level fusion and decision‑level fusion are constructed based on MIDI and text data. The experimental results show that the ERMSLM and ERMBT models can achieve accuracies of 56. 93% and 72. 62% respectively. And the decision‑level multimodal fusion model is more effective. [ABSTRACT FROM AUTHOR]

The article "Richard Wagner and the Art of the Avant-garde, 1860-1910" in the Wagner Journal explores the influence of Wagner's work on late 19th-century artists. The author, Donald A. Rosenthal, delves into the encounter between Wagner and Swiss Symbolist painter Arnold Böcklin, as well as the impact of Wagner's operas on artists like the Pre-Raphaelites, Symbolists, and more. The book covers a wide range of artists and artistic innovations of the period, shedding light on the diverse interpretations and inspirations drawn from Wagner's oeuvre. Despite some criticisms regarding the selection of artists and the number of illustrations, the publication offers a wealth of information for those interested in exploring the intersection of music and visual arts during this period. [Extracted from the article]

Simple Summary: For humans, music is a powerful tool of emotional communication, conveying affective states and modulating physiological states in ways that can influence well-being. Understanding that emotionality is not an exclusively human trait, as it is also recognized in non-human species, it is natural to assume that the intrinsic power of music to modulate the psychophysiological state may be trans-specific. In this way, music can be a powerful tool for enriching the environment and improving the welfare of captive animals, especially farm animals. As there is very limited information on non-human animals, the aim is to review what is known from a human comparative perspective, arguments that support its use, and the potential to use music in non-human species. The effects of sound stimulation as a sensorial environmental enrichment for captive animals have been studied. When appropriately implemented for farm animals, it can improve welfare, health, and productivity. Furthermore, there are indications that music can induce positive emotions in non-human animals, similar to humans. Emotion is a functional state of the organism involving both physiological processes, mediated by neuroendocrine regulation, and changes in behavior, affecting various aspects, including contextual perception and welfare. As there is very limited information on non-human animals, the objective of this review is to highlight what is known about these processes from human biological and comparative perspectives and stimulate future research on using music to improve animal welfare. [ABSTRACT FROM AUTHOR]

The text explores the treatise "Liber de tribus ordinibus" and its relationship to other works on music theory. It suggests that the treatise may have functioned as an independent work or as part of a larger collection called "tres libelli." The authorship of the treatise is attributed to Johannes de Muris, a medieval music theorist. The text provides insights into the content and significance of the treatise, as well as its influence on other works. It also discusses the classification of intervals and the similarities between the treatise and other works on music theory. [Extracted from the article]

The classification of musical emotion is a challenging problem due to the subjectivity of human perception. In order to analyse the emotion contained in music from the perspective of product supply chain management, the psychological model and characteristics of music emotion are introduced. Additionally, the current product supply chain and the composition of the two contracts are analysed based on the characteristics of online music products. Finally, a speech emotion recognition model based on a Fuzzy Cognitive Map (FCM) is proposed. Two corpora containing five emotions (sadness, anger, happiness, surprise, and boredom) are selected to test the performance and characteristics of the network. In order to evaluate the system performance, different acoustic features are extracted, including prosodic features, formant features, marginal spectral features, and Mel‐Frequency Cepstral Coefficients (MFCC). The results show that the average recognition rate of MFCC features on the Berlin emotional speech database is 70.36%, which is 0.75% higher than the second‐highest edge spectrum features and achieves better results. Meanwhile, on this database, the Musical Emotion Recognition System based on FCM (MERS‐FCM) is improved by 20.36%, 7.34%, and 4.12% compared with Back Propagation, K‐nearest neighbour, and support vector machine, respectively, which shows that MERS‐FCM has better performance. The research of the music emotion recognition system based on FCM can provide more reference paths for the use and development of music emotion information. [ABSTRACT FROM AUTHOR]

Music classification has achieved great progress due to the development of Convolutional Neural Networks (CNNs), which is important for music retrieval and recommendation. However, CNN cannot capture temporal information from music audio, which restricts the prediction performance of the model. To address the issue, we propose a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model to learn local spatial features by CNN and learn temporal dependencies by LSTM. In addition, the traditional softmax loss function commonly lacks sufficient discrimination in music classification. Therefore, we propose an additive angular margin and cosine margin softmax (AACM-Softmax) loss function to improve classification results, which minimizes intra-class variances and maximizes inter-class variances simultaneously by enforcing combined margin penalties. Furthermore, we combine the CNN-LSTM model with AACM-Softmax loss function to comprehensively improve the classification performance by learning temporal-dependencies-included discriminative essential features. Extensive experiments on music genre datasets and music emotion datasets show that the proposed model consistently outperforms other models. [ABSTRACT FROM AUTHOR]

Classical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such necessity. Nonetheless, there are limitations, such as the need for large amounts of quality labeled data, a common problem in MER research. To understand the effectiveness of these techniques, a comparison study using various classical machine learning and deep learning methods was conducted. The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1 score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, combining handcrafted features with feature learning. [ABSTRACT FROM AUTHOR]

Music emotion recognition has garnered significant interest in recent years, as the emotions expressed through music can profoundly enhance our understanding of its deeper meanings. The violin, with its distinctive emotional expressiveness, has become a focal point in this field of research. To address the scarcity of specialized data, we developed a dataset specifically for violin music emotion recognition named VioMusic. This dataset offers a precise and comprehensive platform for the analysis of emotional expressions in violin music, featuring specialized samples and evaluations. Moreover, we implemented the CNN–BiGRU–Attention (CBA) model to establish a baseline system for music emotion recognition. Our experimental findings show that the CBA model effectively captures the emotional nuances in violin music, achieving mean absolute errors (MAE) of 0.124 and 0.129. The VioMusic dataset proves to be highly practical for advancing the study of emotion recognition in violin music, providing valuable insights and a robust framework for future research. [ABSTRACT FROM AUTHOR]

Music is omnipresent among human cultures and moves us both physically and emotionally. The perception of emotions in music is influenced by both psychophysical and cultural factors. Chinese traditional instrumental music differs significantly from Western music in cultural origin and music elements. However, previous studies on music emotion perception are based almost exclusively on Western music. Therefore, the construction of a dataset of Chinese traditional instrumental music is important for exploring the perception of music emotions in the context of Chinese culture. The present dataset included 273 10-second naturalistic music excerpts. We provided rating data for each excerpt on ten variables: familiarity, dimensional emotions (valence and arousal), and discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, and transcendence). The excerpts were rated by a total of 168 participants on a seven-point Likert scale for the ten variables. Three labels for the excerpts were obtained: familiarity, discrete emotion, and cluster. Our dataset demonstrates good reliability, and we believe it could contribute to cross-cultural studies on emotional responses to music. [ABSTRACT FROM AUTHOR]

Selecting appropriate musical stimuli to induce specific emotions represents a recurring challenge in music and emotion research. Most existing stimuli have been categorized according to taxonomies derived from general emotion models (e.g., basic emotions, affective circumplex), have been rated for perceived emotions, and are rarely defined in terms of interrater agreement. To redress these limitations, we present research that served in the development of a new interactive online database, including an initial set of 364 music excerpts from three different genres (classical, pop, and hip/hop) that were rated for felt emotion using the Geneva Emotion Music Scale (GEMS), a music-specific emotion scale. The sample comprised 517 English- and German-speaking participants and each excerpt was rated by an average of 28.76 participants (SD = 7.99). Data analyses focused on research questions that are of particular relevance for musical database development, notably the number of raters required to obtain stable estimates of emotional effects of music and the adequacy of the GEMS as a tool for describing music-evoked emotions across three prominent music genres. Overall, our findings suggest that 10–20 raters are sufficient to obtain stable estimates of emotional effects of music excerpts in most cases, and that the GEMS shows promise as a valid and comprehensive annotation tool for music databases. [ABSTRACT FROM AUTHOR]

In this research we have developed devices for hearing impaired people that can transmit the emotions evoked by music. In this way, we will all achieve the full inclusion of impairment people in all social activities. As this is an issue that goes beyond pure engineering research, it has been necessary to create a multidisciplinary group within the Spanish Centre for Captioning and Audio Description (CESyA), in direct collaboration with the Display and Photonic Applications Research Group (GDAF) and the Human Language and Accessibility Technologies Group (HULAT) of the Carlos III University (UC3M) and the Psychiatry and Applied Communication Departments of the Complutense University of Madrid. For this research, a pair of haptic gloves were developed, which transmit different characteristics extracted from music by means of vibrational sequences, intended to drive an emotional reinforcement. Another device, a "music box", a vibration box, was also developed from the idea of using vibration as an instrument. Overall, the acceptance of the glove was positive, with an 80% of users accepting the negative video, repeatedly stating in the surveys that they "felt more when the glove vibrated". The music box was also well received. Volunteers felt that the system gave them a pleasant sensation and that they could feel the music. However, some participants associated the vibration with the sensation of an alarm clock, which was expectedly negative. Considering the obtained EEG results, we can conclude that the integration of simple multimodal stimuli, visual and vibrotactile, helps to enhance the activation of emotional processes linked with musical stimulation. It can also be concluded that multimodal visual and vibrotactile stimulation enhances both attentional and emotional processes. These results pave the way for future developments that will allow a hearing-impaired person to fully enjoy an audiovisual event, which will have a great impact on this group. [ABSTRACT FROM AUTHOR]

This study explores functions of music listening in relation to emotion regulation and identity development in mid-adolescence. Semi-structured interviews were conducted with 17 adolescents (15-16 years old), using a personally selected piece of music as a starting point. Analysis of the interviews focused on descriptions of emotional and identity related functions of their self-chosen music, complexity level of meaning making in participants' autobiographical narratives that emerged in relation to the music they had chosen, and the relation between functions and meaning making level. The results revealed two types of engagement with music entailing a connection between emotional and identity-related functions. The first type involves a more basic form of interaction which is focused on influencing one's mood, and related to feelings of identification with the lively rhythm of the upbeat music they had selected. The second encompasses a more complex emotional regulation, as well as a reflection about the self and their personal history, facilitated by the lyrics or the melody. Adolescents with higher levels of narrative meaning making showed this more complex use of music listening. It is suggested that music listening plays an important role in the interplay between emotional regulation and identity development in mid-adolescence. [ABSTRACT FROM AUTHOR]

Music festivals are meant to create a unique experience for those who attend them, and this occurs through multiple phenomena. Among these phenomena are various cultural identification processes, which may be directly connected to festivalgoers' emotions. In this scope, the main aim of this research is to understand how music festivals trigger, shape, reinforce and influence cultural identification processes and how these processes relate to the emotions felt by festivalgoers. In order to attain this aim, we present the academic state of the art in terms of models for evaluating intangible impacts and emotions in different settings. We then propose a model and a mixed-method approach for analysing such impacts on music festival experiences. This model is based on attendees' perception of cultural impacts and music emotions during these experiences. Finally, we apply these different methodological approaches to the study of a classical music festival experience. We then draw conclusions about the relationship between festivalgoers' emotions and identification processes. Specifically, there is a clear relationship between intense positive emotions and regional identification processes, cultural skills development and growth in music listening, where the festival experience encourages attendees to add new music to their musical preferences. [ABSTRACT FROM AUTHOR]

We came across many applications that are serve solutions and assistance for the music playlist creation recommendation. To provide more facility and the solutions to the music creation and recommendation of music and numerous technique approaches have followed in our proposed system. To determine the approached and developing the current state of human emotion and playing through the mood determined by the system. Our proposed system mainly focuses on playing and recommending the songs according to end user current mood or betterment of the mood. [ABSTRACT FROM AUTHOR]

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The construction of opera scenarios involves the development of a dramatic and narrative framework upon which an opera is built. These scenarios serve as the foundation for the libretto, music, staging, and overall production of the opera. In this introduction, we will explore the principles and procedures that guide the creation of opera scenarios. In summary, constructing an opera scenario is a complex and collaborative process that involves the careful consideration of narrative, character, music, and cultural context. The principles and procedures outlined here provide a foundation for the creation of opera scenarios that can captivate audiences and convey the depth of human emotion through music and drama. Over time, the evolution of the singing voice follows a phenomenon of symbiosis between words and music. The intertwining of the act of speaking with that of singing is constantly found in the concerns of the creators in ancient monody, Greek declamation, Gregorian chant, in the polyphonic style, in the opera performance as well as in all the ramifications of the vocal genre. [ABSTRACT FROM AUTHOR]

Multi-label classification is a hot topic in the field of data mining. It has important applications in text classification, image, video annotation, music emotion classification, and other fields. In the past, most papers only used label correlation or feature screening to improve the accuracy of the multi-label classification and paid one-sided attention to feature screening while ignoring the correlation between labels. Therefore, in this paper, a multi-classification algorithm (MIRD) combining feature screening and label correlation is proposed, which not only combines the correlation between labels and features and design thresholds to screen features, but also uses association rules to update the label set to realize the correlation between labels, making full use of the correlation for multi-label classification. Finally, the proposed algorithm is compared with other multi-label algorithms, and the results show that it can achieve better results from most data sets, which proves that the proposed algorithm is better than the comparison algorithm. [ABSTRACT FROM AUTHOR]

This paper explores the intricate relationship between music and emotions through a neurological perspective. We present a methodology to enhance music recommendation systems by integrating Facial Emotion Recognition (FER) through the YOLOv8 algorithm. Our aim is to develop a robust recommendation framework that seamlessly integrates Content-Based Filtering and Collaborative Filtering techniques with FER to facilitate real-time adaptability. Using natural language processing and machine learning concepts, we utilize TF-IDF for genre analysis and cosine similarity for quantifying song similarity. Singular Value Decomposition is used to facilitate User-Based Collaborative Filtering, while k-Nearest Neighbors is employed for Item-Based Collaborative Filtering. By extracting a dataset from Spotify, our recommendation system harnesses a rich source of music-related information. Our YOLOv8 model achieved 86.20% accuracy rate with the FER+ dataset. We also discuss future research directions aimed at addressing emotional recognition limitations, enhancing dataset diversity, and implementing comprehensive evaluation metrics for robust performance assessment. [ABSTRACT FROM AUTHOR]