Classification of musical hallucinations and the characters along with neural-molecular mechanisms of musical hallucinations associated with psychiatric disorders

BACKGROUND Musical hallucinations(MH)involve the false perception of music in the absence of external stimuli which links with different etiologies.The pathomechanisms of MH encompass various conditions.The etiological classification of MH is of particular importance and offers valuable insights to understand MH,and further to develop the effective treatment of MH.Over the recent decades,more MH cases have been reported,revealing newly identified medical and psychiatric causes of MH.Functional imaging studies reveal that MH activates a wide array of brain regions.An up-to-date analysis on MH,especially on MH comorbid psychiatric conditions is warranted.AIM To propose a new classification of MH;to study the age and gender differences of MH in mental disorders;and neuropathology of MH.METHODS Literatures searches were conducted using keywords such as“music hallucination,”“music hallucination and mental illness,”“music hallucination and gender difference,”and“music hallucination and psychiatric disease”in the databases of PubMed,Google Scholar,and Web of Science.MH cases were collected and categorized based on their etiologies.The t-test and ANOVA were employed(P<0.05)to compare the age differences of MH different etiological groups.Function neuroimaging studies of neural networks regulating MH and their possible molecular mechanisms were discussed.RESULTS Among the 357 yielded publications,294 MH cases were collected.The average age of MH cases was 67.9 years,with a predominance of females(66.8%females vs 33.2%males).MH was classified into eight groups based on their etiological mechanisms.Statistical analysis of MH cases indicates varying associations with psychiatric diagnoses.CONCLUSION We carried out a more comprehensive review of MH studies.For the first time according to our knowledge,we demonstrated the psychiatric conditions linked and/or associated with MH from statistical,biological and molecular point of view.

Emotionally Resonant Branding: The Role of AI in Synthesising Dynamic Brand Images for Artists in the Music Industry

Artificial Intelligence (AI) expands its recognition rapidly through the past few years in the context of generating content dynamically, remarkably challenging the human creativity. This study aims to evaluate the efficacy of AI in enhancing personal branding for musicians, particularly in crafting brand images based on emotions received from the artist’s music will improve the audience perceptions regarding the artist’s brand. Study used a quantitative approach for the research, gathering primary data from the survey of 191 people—music lovers, musicians and music producers. The survey focuses on preferences, perceptions, and behaviours related to music consumption and artist branding. The study results demonstrate the awareness and understanding of AI’s role in personal branding within the music industry. Also, results indicate that such an adaptive approach enhances audience perceptions of the artist and strengthens emotional connections. Furthermore, over 50% of the participants indicated a desire to attend live events where an artist’s brand image adapts dynamically to their emotions. The study focuses on novel approaches in personal branding based on the interaction of AI-driven emotional data. In contrast to traditional branding concepts, this study indicates that AI can suggest dynamic and emotionally resonant brand identities for artists. The real time audience response gives proper guidance for the decision-making. This study enriches the knowledge of AI’s applicability to branding processes in the context of the music industry and opens the possibilities for additional advancements in building emotionally appealing brand identities.

The Application and Development of Music Therapy in Rehabilitation Medicine

Music therapy,as an ancient and continually evolving therapeutic approach,has demonstrated unique effects and extensive potential applications in the field of rehabilitation medicine.This paper first explores the physiological and psychological impacts of music and theoretical models of its therapeutic mechanisms.It further details the specific applications of music therapy in neurological rehabilitation,motor function recovery,psychological and emotional adjustment,and chronic disease and pain management.The article also investigates the prospects of integrating music therapy with modern technologies such as virtual reality and artificial intelligence and emphasizes the importance of interdisciplinary collaboration and policy support in advancing this field.Through comprehensive analysis,the paper identifies future development directions and research needs for music therapy in rehabilitation medicine.

Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been employed.Analysis of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%nega-tive tweets expressed sadness,fear are of low intensity.A qualitative analysis has also been conducted and the topics detected are grouped into themes such as eco-nomic impacts,case reports,treatments,entertainment and vaccination.The results of analysis show that the issues related to the pandemic are expressed dif-ferent emotions in twitter which helps in interpreting the public insights during the pandemic and these results are beneficial for planning the dissemination of factual health statistics to build the trust of the people.The performance comparison shows that the proposed IBEC-CNN model outperforms the conventional models and achieved 83.71%accuracy.The%of COVID-19 tweets that discussed the different topics vary from 7.45%to 26.43%on topics economy,Statistics on cases,Government/Politics,Entertainment,Lockdown,Treatments and Virtual Events.The least number of tweets discussed on politics/government on the other hand the tweets discussed most about treatments.

Construction of Psychological Adjustment Function Model of Music Education Based on Emotional Tendency Analysis

In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.

Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.

Study on the fusion emotion classification of multiple characteristics based on attention mechanism

The current research on emotional classification uses many methods that combine the attention mechanism with neural networks.However,the effect is unsatisfactory when dealing with complex text.An emotional classification model is proposed,which combines multi-head attention(MHA)with improved structured-self attention(SSA).The model makes several different linear transformations of input by introducing MHA mechanism and can extract more comprehensive high-level phrase representation features from the word embedded vector.Meanwhile,it can realize the parallelization calculation and ensure the training speed of the model.The improved SSA structure uses matrices to represent different parts of a sentence to extract local key information,to ensure that the degree of dependence between words is not affected by time and sentence length,and generate the overall semantics of the sentence.Experiment results show that the current model effectively obtains global structural information and improves classification accuracy.

On the importance of emotional cultivation in vocal music teaching

All teaching have feelings, no feelings of the teaching and learning are short, no matter how much knowledge, or repeatedmemory of how many times, if there is no emotional investment, these will vanish with time, the vocal music teaching is no exception. A lot ofpeople for the understanding of teaching the subject is superficial, that is, the vocal music teaching practice exercises, teach students the correctpronunciation, protect their voice and so on, is not to say how much these cognitive errors, but is too one-sided, if people like this kind of thought,then the vocal music teaching need emotional training？ The vocal music teaching, the cultivation of emotion is not only to make students morerelaxed in learning, but also to let them realize the subject learning fun, learn to take the initiative to learn, so that we can reach our pursuit of ＂liveand learn＂ realm. This article focuses on the importance of emotional education in vocal music teaching.

Individual Classification of Emotions Using EEG

Many studies suggest that EEG signals provide enough information for the detection of human emotions with feature based classification methods. However, very few studies have reported a classification method that reliably works for individual participants (classification accuracy well over 90%). Further, a necessary condition for real life applications is a method that allows, irrespective of the immense individual difference among participants, to have minimal variance over the individual classification accuracy. We conducted offline computer aided emotion classification experiments using strict experimental controls. We analyzed EEG data collected from nine participants using validated film clips to induce four different emotional states (amused, disgusted, sad and neutral). The classification rate was evaluated using both unsupervised and supervised learning algorithms (in total seven “state of the art” algorithms were tested). The largest classification accuracy was computed by means of Support Vector Machine. Accuracy rate was on average 97.2%. The experimental protocol effectiveness was further supported by very small variance among individual participants’ classification accuracy (within interval: 96.7%, 98.3%). Classification accuracy evaluated on reduced number of electrodes suggested, consistently with psychological constructionist approaches, that we were able to classify emotions considering cortical activity from areas involved in emotion representation. The experimental protocol therefore appeared to be a key factor to improve the classification outcome by means of data quality improvements.

Analysis of the trend of global power sources based on comment emotion mining

In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations;however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.

Can music improve the symptoms of stable angina? A randomized controlled trial

Worldwide, the leading cause of death is ischemic heart disease. Other than medical and surgical management, alternative therapy such as relaxing music has been identified as having an impact on reducing morbidity in ischemic heart disease. Although several studies have been conducted to find out the impact of music on pain, anxiety, heart rate and stress in myocardial ischaemia, literature on the long term impact of music on severity of symptoms associated with stable angina is very sparse. Therefore, the whole purpose of this study was to determine the long term effects of Indian music on severity of symptoms in patients with stable angina. Methodology: A single blind randomized clinical trial was conducted on 60 patients of 45 to 65 years of age with stable angina. Intervention group (n = 30) listened to a music based on Indian classical system at home twice a day complementary to their regular treatment for a period of one month. Control group (n = 30) was only on their usual treatment. Both groups were assessed prior and one month after the study period for severity of symptoms based on Canadian classification of angina guidelines and their treatment. Results: Severities of symptoms (timing of the chest pain, chest pain during walking and climbing a staircase, the effect of chest pain in day to day physical activities, frequency and the number of GTN used per week and frequency of consultation a doctor for chest pain) were significantly improved in the study group (p < 0.05, p < 0.01) after intervention. However, the control group did not show any significant changes (p > 0.05). Conclusion: Systematically, regular listening of music based on Indian classical system significantly improves the severity of the stable angina symptoms. Hence music has a potential benefit in considering for use as complementary to angina treatment in reducing morbidity.

Application of five-element music therapy in pain coping skills training in patients with knee osteoarthritis

Objective:The objective of this study is to assess the application effect of five elements music therapy introduced in the pain coping skills training of knee osteoarthritis(KOA).Materials and Methods:Totally,80 patients with KOA were selected and randomly divided into the experimental group(39 cases)and the control group(41 cases).The control group was only given routine nursing measures,and the experimental group was additionally treated with five-element music therapy on the basis of the control group,twice a day,28 days in total.The Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)was used to evaluate the functional status of the knee joint of the two groups.The clinical efficacy of the two groups was evaluated by Guiding Principles for Clinical Research of New Chinese Medicine in the Treatment of Osteoarthritis.Results:WOMAC score statistically significantly decreased in the experimental group(35.92±9.48 vs.16.17±5.43,P<0.01)and the control group(36.73±6.42 vs.22.53±7.51,P<0.01)after 28 days of intervention when compared with that before intervention;WOMAC score in the experimental group was lower than that of the control group after 28 days of intervention(16.17±5.43 vs.22.53±7.51,P<0.01).The total effective rate of the experimental group was statistically higher than that of the control group(82.0%vs.51.2%,χ2=11.97,P=0.003).Conclusion:The combination of five-element music therapy and routine nursing measures has better effect in relieving pain and bad emotions of patients with KOA when compared with routine nursing measures alone.

A NEW SVM BASED EMOTIONAL CLASSIFICATION OF IMAGE

How high-level emotional representation of art paintings can be inferred from percep tual level features suited for the particular classes (dynamic vs. static classification)is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.

Music in depression: Neural correlates of emotional experience in remitted depression

AIM: To investigate neural and behavioral correlates of emotional experiences as potential vulnerability markers in remitted depression. METHODS: Fourteen remitted participants with a history of major depression and fourteen closely matched healthy control participants took part in the study. We used two psychiatric interviews(Hamilton Depression Rating Scale, Montgomery-Asberg Depression Rating Scale) and one self-report scale(Beck Depression Inventory) to assess remission. Healthy control participants were interviewed by an experienced psychiatrist to exclude those who showed any current or lifetime psychiatric or neurological disorders. To explore psychosocialand cognitive-interpersonal underpinnings of potential vulnerability markers of depression, early life stress, coping styles and alexithymia were also assessed. We induced pleasant and unpleasant emotional states using congruent combinations of music and human emotional faces to investigate neural and behavioral correlates of emotional experiences; neutral stimuli were used as a control condition. Brain responses were recorded using functional magnetic resonance imaging. Behavioral responses of pleasantness, arousal, joy and fear were measured via button-press inside the resonance imaging scanner. RESULTS: The mean age of the sample was 54.9(± 11.3) years. There were no differences between remitted depressed(RD)(n = 14; 9 females and 5 males) and healthy participants(n = 14; 8 females and 6 males) regarding age, current degree of depression, early life stress, coping styles and alexithymia. On a neural level, RD participants showed reduced activations in the pregenual anterior cingulate cortex(pg ACC) in response to pleasant [parameter estimates:-0.78 vs 0.32; t(26) =-3.41, P < 0.05] and unpleasant [parameter estimates:-0.88 vs 0.56; t(26)=-4.02, P < 0.05] emotional stimuli. Linear regression analysis revealed that pg ACC activity was modulated by early life stress [β =-0.48; R2 = 0.23, F(1,27) = 7.83, P < 0.01] and taskoriented coping style [β = 0.63; R2 = 0.37, F(1,27) = 16.91, P < 0.001]. Trait anxiety modulated hippocampal responses to unpleasant stimuli [β = 0.62; R2= 0.38, F(1,27) = 15.95, P < 0.001]. Interestingly, in their reported experiences of pleasantness, arousal, happiness and fear in response to pleasant, unpleasant and neutral stimuli, RD participants did not differ significantly from healthy control participants. Adding trait anxiety or alexithymia as a covariate did not change the results.CONCLUSION: The present study indicates that, in euthymic individuals, depression history alters neural correlates, but not the subjective dimension of pleasant and unpleasant emotional experiences.

Electroencephalographic Study of Gamma Rhythm in the Autobiographical Memory Evocation Mediated by Musical Stimuli

Listening to music, or part of it, may stir the memory of a past moment, along with its associated emotions, such occurrences are known as autobiographical memories. Electroencephalographic (EEG) studies have shown alterations in memory recall and musical processing. However, no research was found showing a relation among music, autobiographical memories and associated emotions. The purpose of this study was to identify cortical areas involved in the evocation of autobiographical memory (associated with positive and negative events) mediated by musical stimuli. For that, gamma rhythm was analyzed through EEG recordings performed by 45 male volunteers while they were submitted to two stimuli: 1) the music capable of recalling memories associated to a positive event;2) the music capable of evoking memories associated to a negative event. Gamma band analysis was used in search of greater brain electrical activity. As results, researchers observed increased activity in right brain hemisphere during the musical processing, besides its hypoactivation when volunteers were submitted to musical stimuli related to memories of negative events.

Memory and Aesthetics： Study of Musical Quotations in Ives’s and Crumb’s Music

Throughout Western music history, pre-existing material has long been the aesthetic core of a new composition. Yet there has never been such an epoch as our time in which using pre-existing material, melodic quotation in particular, features so extensively in works of many of the composers. The aim of this paper is to investigate how the use of quoted tunes in a musical piece operates in an interwoven complex where time and space are of the essence. A quote is able to oscillate perpetually between one’s mental worlds of the memorable past and the imaginative present when it is highlighted enough to be recognizable from its surrounding context. Upon interpreting the use of quotation in various contexts, the aesthetic object, I argue, is the shift from original to quoted music, and vice versa. And listeners can respond aesthetically to the quotation itself even without knowledge of its provenance and textual or referential content.

Music Genre Classification Using DenseNet and Data Augmentation

It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation,distribution,and enjoyment of musical works have undergone huge changes.As the number ofmusic products increases daily and themusic genres are extremely rich,storing,classifying,and searching these works manually becomes difficult,if not impossible.Automatic classification ofmusical genres will contribute to making this possible.The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive(FMA)data set.For Small FMA,it is more efficient to augment the data by generating an echo rather than pitch shifting.The research results show that the DenseNet121 model and data augmentation methods,such as noise addition and echo generation,have a classification accuracy of 98.97%for the Small FMA data set,while this data set lowered the sampling frequency to 16000 Hz.The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.

基于MUSIC算法的OFDM系统参数盲估计仿真研究

在介绍正交频分复用(OFDM)技术基本原理和基本接收模型的基础上,提出将空间谱估计中的多重信号分类(MUSIC)算法应用到OFDM系统参数盲估计中。经过MATLAB实验仿真表明,在没有信号先验知识的情况下,此算法对信噪比较低的仿真OFDM信号和实际802.11a信号均能有效估计出其子载波个数和发射端作傅立叶反变换的点数。

Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image.Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image.However,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset.Therefore,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation.To perform more accurate emotional region prediction,this study designs a multilayer attentional interaction module.The module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention module.Through layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal scale.To complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation module.The module uses global average pooling to compress spatial information and connect channel information from all layers.Then,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each layer.Eventually,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost information.To reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small dataset.The experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods,with accuracies of 90.27%,84.66%and 84.96%.