Music Emotion Research Articles

Regulating test anxiety by aesthetic musical emotions: based on the mutual promotion and mutual counteraction (MPMC) theory of affect

Regulating test anxiety by aesthetic musical emotions: based on the mutual promotion and mutual counteraction (MPMC) theory of affect

Neural correlates of musical timbre: an ALE meta-analysis of neuroimaging data.

Timbre is a central aspect of music that allows listeners to identify musical sounds and conveys musical emotion, but also allows for the recognition of actions and is an important structuring property of music. The former functions are known to be implemented in a ventral auditory stream in processing musical timbre. While the latter functions are commonly attributed to areas in a dorsal auditory processing stream in other musical domains, its involvement in musical timbre processing is so far unknown. To investigate if musical timbre processing involves both dorsal and ventral auditory pathways, we carried out an activation likelihood estimation (ALE) meta-analysis of 18 experiments from 17 published neuroimaging studies on musical timbre perception. We identified consistent activations in Brodmann areas (BA) 41, 42, and 22 in the bilateral transverse temporal gyri, the posterior superior temporal gyri and planum temporale, in BA 40 of the bilateral inferior parietal lobe, in BA 13 in the bilateral posterior Insula, and in BA 13 and 22 in the right anterior insula and superior temporal gyrus. The vast majority of the identified regions are associated with the dorsal and ventral auditory processing streams. We therefore propose to frame the processing of musical timbre in a dual-stream model. Moreover, the regions activated in processing timbre show similarities to the brain regions involved in processing several other fundamental aspects of music, indicating possible shared neural bases of musical timbre and other musical domains.

Commentary on Frank et al.'s "Exploring the variability of musical-emotional expression over historical time"

Music is often regarded as the 'language of the emotions' (Cooke, 1995), and since the early 20th century, empirical research into musical emotions has been conducted to explore the mystery of how they are evoked by music. In this context, Frank et al. presented an experimental study that examined the scarcely researched field of historical listening. Their study aimed to investigate the question, "Do modern listeners hear the emotional content in Baroque music that the composer intended to portray?". The results indicated that modern listeners placed the three modern excerpts in the expected quadrants of the valence-arousal space. However, there were significantly different valence and arousal ratings among the Baroque examples and the modern excerpts, and significant differences between paired examples (where Baroque and modern examples were expected to fall into the same quadrants) occurred. This commentary summarizes Frank et al.'s experimental study, discusses methodological considerations, and suggests possible refinements for future (experimental) studies on historical listening.

Exploring the variability of musical-emotional expression over historical time

A listening experiment was designed to test whether modern listeners perceive the same affective content in Baroque music as the composer intended to portray. Listeners rated three musical examples from Johann David Heinichen’s 1728 treatise Der General-Bass in der Composition for valence and arousal. Examples were chosen based on descriptions by the composer in which he outlined their intended affective content. Results showed a significant mismatch between original descriptions and listener ratings, indicating a change in the perceived affective content of the music. The historical variability of musical-emotional expression in general, with a focus on the role of structural emotion cues (particularly mode), is discussed, closing with suggestions for future research in the area of historical musical emotion

Analysis of the realization for emotion recognition based on machine learning

As a matter of fact, music emotion recognition based on machine learning has been a trend in recent years. This study describes the analysis of the realization for emotion recognition based on machine learning. In this paper, the study expresses some methods of analyzing the emotion of the music composition. The paper represents some classifier molds that enable us to determine the emotion of the music. It aims to provide readers with a deeper comprehension of the music and composers feelings and to know more about the background of the music. The study may let more people to improve their own ability to appreciate music, if this process has let great amounts of individuals known. It also allows people to have more awareness and feelings with more voices. In this paper, the research will also show some about the evolution and original of the synthesizer. Moreover, the study will go to illustrate this through scientific, real examples and personal experiences and pictures.

Analysis and recognition of a novel experimental paradigm for musical emotion brain-computer interface

Musical emotions have received increasing attention over the years. To better recognize the emotions by brain-computer interface (BCI), the random music-playing and sequential music-playing experimental paradigms are proposed and compared in this paper. Two experimental paradigms consist of three positive pieces, three neutral pieces and three negative pieces of music. Ten subjects participate in two experimental paradigms. The features of electroencephalography (EEG) signals are firstly analyzed in the time, frequency and spatial domains. To improve the effect of emotion recognition, a recognition model is proposed with the optimal channels selecting by Pearson’s correlation coefficient, and the feature fusion combining differential entropy and wavelet packet energy. According to the analysis results, the features of sequential music-playing experimental paradigm are more different among three emotions. The classification results of sequential music-playing experimental paradigm are also better, and its average results of positive, neutral and negative emotions are 78.53%, 72.81% and 77.35%, respectively. The more obvious the changes of EEG induced by the emotions, the higher the classification accuracy will be. After analyzing two experimental paradigms, a better way for music to induce the emotions can be explored. Therefore, our research offers a novel perspective on affective BCIs.

The Role of Sound in the Immersive Experience

To create an immersive experience, it is not important to have a plethora of stimuli for different sensors, but to have their coherence, or in other words, multi-sensory integration. This coherence becomes the necessary basis for immersing oneself in a story, a game, a movie, or any other experience. Sound plays a key role in creating such an experience, both by accompanying the action and emphasizing the narrative flow, and as an independent actor that takes a central role and directs attention. The aim of the paper and poster is to explore the means by which sound can influence immersion in computer-generated virtual environments and the psychological mechanisms involved in the musical induction of emotions. The main question to be answered is how these techniques and models can be used to create a sense of immersion. The first part of the article examines immersiveness itself. The second part is devoted to the practical aspects of sound design for immersive technologies, describing the tools and techniques used to create realistic and compelling soundscapes. The third part touches briefly on the psychological mechanisms behind the induction of musical emotions. Taking a holistic view of these components and using them to reveal each other is how sound can create a sense of presence and place.

MusicEmo: transformer-based intelligent approach towards music emotion generation and recognition

MusicEmo: transformer-based intelligent approach towards music emotion generation and recognition

Construction of a Chinese traditional instrumental music dataset: A validated set of naturalistic affective music excerpts

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.

Comparison and analysis of prediction accuracy between traditional machine learning algorithms and XGBoost algorithm in music emotion classification

Music emotion classification refers to determining the emotional types of music, such as happiness, sadness, anger, and passion, based on aspects like rhythm, melody, and tone. The development of artificial intelligence can be applied to music recommendation by employing machine learning algorithms to ascertain emotional attributes of music, enabling precise music suggestions for users to enhance their musical experience. This paper compares and analyzes the classification performance of traditional Random Forest machine learning algorithms and the XGBoost model on a Turkish music emotion dataset. We selected a comprehensive dataset from Kaggle, containing a vast array of music samples and their corresponding emotional labels, making it an extensive music emotion classification dataset. From the experimental results, it's evident that the traditional Random Forest machine learning model outperforms the XGBoost model in terms of accuracy, precision, and recall. The accuracy of the traditional Random Forest machine learning model stands at 80.8%, whereas the XGBoost model's accuracy is 75%. The recall rate for the traditional Random Forest machine learning model is 80.8%, while for XGBoost, it's 77.2%. The F1 score for the traditional Random Forest machine learning model is 80.5%, whereas for XGBoost, it's 75.3%. These parameters indicate that the traditional Random Forest machine learning model exhibits superior predictive performance in music emotion classification. However, the XGBoost model possesses its own advantages, such as faster learning and prediction speeds, high accuracy, and strong generalization capabilities. In summary, the traditional Random Forest machine learning model demonstrates better robustness and interpretability, effectively handling samples with noise and missing data, thus finding widespread practical application. On the other hand, the XGBoost model excels in rapid training and prediction, coupled with higher accuracy and versatility, making it advantageous in dealing with large datasets. The research outcomes of this paper hold significant importance for the study and application of music emotion classification. The experimental results presented herein offer valuable insights for researchers and practitioners, aiding them in selecting appropriate machine learning models, optimizing, and adjusting them to achieve the best classification results.

“Research on Music Emotion Analysis and Dance Creation Based on Neural Network”

The one-of-a-kind avenues for self-expression that dancing provides as a form of physical art. Movements of the body, both internal and external, and other forms of expression make up the bulk of a dancer's lexicon. Through passive observation, the viewer is able to catch up on the dancer's intended meaning through their body language. It's the one-of-a-kind way that each dancer finds to convey feeling and story through their body. In this piece, we intelligently oversee dance training and take on challenging nonlinear control problems by combining AI technology and the BP neural networks (BPNN) approach.In this study, the training dance teachers receive in dance language, talking music for dancing, and stage art was evaluated using the BPNN technique and the PCA-BPNN strategy. Median accuracy for the BPNN research model is 85.35 percent as age 80 approaches, whereas for the PCA-BPNN research model it is just 65.64 percent. This demonstrates that the BPNN grading model provides more precise results than the PCA-BPNN model. Using BPNN algorithm-based dancing performed within the context of artificial intelligence technology is one way to attain two goals: the spread and pleasure of beauty, and the harmonious combination of athletics and the arts. These two aims are not mutually exclusive.

MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion Classification

Music plays a vital role in human culture and society, serving as a universal form of expression. However, accurately classifying music emotions remains challenging due to the intricate nature of emotional expressions in music and the integration of diverse data sources. To address these challenges, we propose the Multilayered Music Decomposition and Multimodal Integration Interaction (MMD-MII) model. This model employs cross-processing to facilitate interaction between audio and lyrics, ensuring coherence in emotional representation. Additionally, we introduce a hierarchical framework based on the music theory, focusing on the main and chorus sections, with the chorus processed separately to extract precise emotional representations. Experimental results on the DEAM and FMA datasets demonstrate the effectiveness of the MMD-MII model, achieving accuracies of 49.68% and 49.54% respectively. Compared with the existing methods, our model outperforms in accuracy and F1 scores, offering promising implications for music recommendation systems, healthcare, psychology, and advertising, where accurate emotional analysis is essential.

Chinese Version of the Short Geneva Emotional Music Scale: Cross-cultural Adaptation and Psychological Measurement

In this study we developed the Chinese Geneva Emotion Music Scale (GEMS-C) and a simplified version (S-GEMS-C) verified by Chinese native speakers. As the GEMS is a one-word-item scale without context, we performed an expert revision process after forward-backward translation. The results were optimized through structured interviews to obtain the final Chinese GEMS (GEMS-C). We reduced the number of GEMS items to form a short version (S-GEMS-C). The GEMS-C and the S-GEMS-C were shown to have high reliability, construct validity, sensitivity, concurrent validity, and discriminant validity. Additionally, this study found a high correlation between the GEMS-C, S-GEMS-C, and OMES-M, and the difference between familiarity with the music and the degree of emotional arousal.

Violin Music Emotion Recognition with Fusion of CNN–BiGRU and Attention Mechanism

Violin Music Emotion Recognition with Fusion of CNN–BiGRU and Attention Mechanism

Emotional Responses to Music: The Essential Inclusion of Emotion Adaptability and Situational Context

The link between music and emotion, as articulated from a cognitive perspective, assumes that music carries expressive cues that convey or induce emotional responses in listeners. Studies following this paradigm often investigate how responses converge or diverge among individuals, social groups, and cultures. However, results vary from one study to another, with few satisfactory explanations as to why. We contend that emotional responses to music are adaptable, arising from a conscious and subconscious continuous processing of the overarching situational context and its interaction with psychophysical, cultural, and personal variables. By integrating theory and data from multiple domains, we present the Framework for Adaptable Musical Emotions (FAME), which explains emotional responses to music through the mechanism of emotion adaptability on a continuum of evolutionary to fleeting time frames. FAME represents an advance on models of music and emotion that primarily focus on decoding emotional signals from the sounded music. FAME provides the first basis for predictions of emotional adaptability and situational context and may explain previously observed variability in emotional responses to music, guiding future research, and novel understandings.

Design of Music Emotion Analysis and Creation Aid System Based on Machine Learning

In human-centric contexts, visual vision plays an increasingly important role in decision making, learning, communication, and situation awareness. Music is a language that conveys emotion to all creatures, including plants and animals. These emotional characteristics are included in the emotion found in music, along with other feelings conveyed via melodies, rhythms, and composer-highlighted moments. Human interaction, communication, decision-making, and cognitive processes are all heavily influenced by emotion. However, Subjective music emotion classification is made more difficult by pure machine classification due to its objectivity; additionally, emotion classification has not reached the same level of accuracy as style classification due to the complex and poorly understood mechanisms underlying how humans perceive music and generate emotions. This manuscript proposes a Multi-Scale Adaptive Graph Neural Network (MSAGNN)optimized with the Remora Optimization Algorithm (ROA) for predicting music emotion MEA-MSAGNN-ROA. Initially data is taken from Music Video Emotion (MVE) dataset Afterward the data is fed to Attribute-based Neural Collaborative Filtering (AbNCF) based pre-processing process. The outcome from the pre-processing data is transferred to the MSAGNN. The music emotions are successfully classified by using MSAGNN are exciting, fear, sad and relaxation. The ROA is used to optimize the weight parameter of MSAGNN. The proposed MEA-MSAGNN-ROA is applied in Python working platform. Performance parameters, like accuracy, precision, sensitivity, F1-score, ROC and recall analyzed to compute proposed approach. The proposed MEA-MSAGNN-ROA approach yields improved results in terms of accuracy (22.46%, 38.58%, 21.74%), sensitivity (21.97%, 33.88%, 25.52%), and precision. reduced computation times of 21.86%, 36.76%, 28.95%, and 81.46%, 95.97%, 86.77%.The proposed MEA-MSAGNN-ROA method is compared with the existing methods such as MEA-CNN, MEA-BPNN, and MEA-FNN, respectively. From the result it is concludes that the proposed MEA-MSAGNN-ROA method based accuracy is higher than the existing methods.

A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition.

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.

Music emotion recognition based on temporal convolutional attention network using EEG.

Music is one of the primary ways to evoke human emotions. However, the feeling of music is subjective, making it difficult to determine which emotions music triggers in a given individual. In order to correctly identify emotional problems caused by different types of music, we first created an electroencephalogram (EEG) data set stimulated by four different types of music (fear, happiness, calm, and sadness). Secondly, the differential entropy features of EEG were extracted, and then the emotion recognition model CNN-SA-BiLSTM was established to extract the temporal features of EEG, and the recognition performance of the model was improved by using the global perception ability of the self-attention mechanism. The effectiveness of the model was further verified by the ablation experiment. The classification accuracy of this method in the valence and arousal dimensions is 93.45% and 96.36%, respectively. By applying our method to a publicly available EEG dataset DEAP, we evaluated the generalization and reliability of our method. In addition, we further investigate the effects of different EEG bands and multi-band combinations on music emotion recognition, and the results confirm relevant neuroscience studies. Compared with other representative music emotion recognition works, this method has better classification performance, and provides a promising framework for the future research of emotion recognition system based on brain computer interface.

Medical Insights into the Artistic Presentation of Integrating Public Environment and Health into Music Emotion

Medical Insights into the Artistic Presentation of Integrating Public Environment and Health into Music Emotion

MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion

Generating music with emotion is an important task in automatic music generation, in which emotion is evoked through a variety of musical elements (such as pitch and duration) that change over time and collaborate with each other. However, prior research on deep learning-based emotional music generation has rarely explored the contribution of different musical elements to emotions, let alone the deliberate manipulation of these elements to alter the emotion of music, which is not conducive to fine-grained element-level control over emotions. To address this gap, we present a novel approach employing musical element-based regularization in the latent space to disentangle distinct elements, investigate their roles in distinguishing emotions, and further manipulate elements to alter musical emotions. Specifically, we propose a novel VQ-VAE-based model named MusER. MusER incorporates a regularization loss to enforce the correspondence between the musical element sequences and the specific dimensions of latent variable sequences, providing a new solution for disentangling discrete sequences. Taking advantage of the disentangled latent vectors, a two-level decoding strategy that includes multiple decoders attending to latent vectors with different semantics is devised to better predict the elements. By visualizing latent space, we conclude that MusER yields a disentangled and interpretable latent space and gain insights into the contribution of distinct elements to the emotional dimensions (i.e., arousal and valence). Experimental results demonstrate that MusER outperforms the state-of-the-art models for generating emotional music in both objective and subjective evaluation. Besides, we rearrange music through element transfer and attempt to alter the emotion of music by transferring emotion-distinguishable elements.