Types Of Music Research Articles

The (Co)Evolution of Language and Music Under Human Self-Domestication

Together with language, music is perhaps the most distinctive behavioral trait of the human species. Different hypotheses have been proposed to explain why only humans perform music and how this ability might have evolved in our species. In this paper, we advance a new model of music evolution that builds on the self-domestication view of human evolution, according to which the human phenotype is, at least in part, the outcome of a process similar to domestication in other mammals, triggered by the reduction in reactive aggression responses to environmental changes. We specifically argue that self-domestication can account for some of the cognitive changes, and particularly for the behaviors conducive to the complexification of music through a cultural mechanism. We hypothesize four stages in the evolution of music under self-domestication forces: (1) collective protomusic; (2) private, timbre-oriented music; (3) small-group, pitch-oriented music; and (4) collective, tonally organized music. This line of development encompasses the worldwide diversity of music types and genres and parallels what has been hypothesized for languages. Overall, music diversity might have emerged in a gradual fashion under the effects of the enhanced cultural niche construction as shaped by the progressive decrease in reactive (i.e., impulsive, triggered by fear or anger) aggression and the increase in proactive (i.e., premeditated, goal-directed) aggression.

Simulation research on music feature recognition based on mobile big data and smart sensors

Abstract With the increasing importance of music information retrieval, the construction of effective music recognition methods has gradually become one of the research focuses. The vocal map features of the collected music pronunciation signals are extracted. The research in this paper is primarily based on the basic physical characteristics of music notes; once these characteristics are identified, then they are mathematically extracted and analysed (voiceprint feature method), and a recognition model is established. On this basis, the process of adaptive separation and recognition of music signals is completed. Finally, the performance of different recognition types is verified and evaluated through experiments. The results show that the average accuracy rate of the modified algorithm within a certain range reaches 73.6%; additionally, the average accuracy rate is increased by 10.95% compared with the audio recognition based on Internet of Things (IoT) data, and is more accurate than the audio recognition method based on data collection, showing an improvement of 20.75%. This shows that the modified recognition algorithm adopted in the present research has stable and high accuracy for comprehensive music types with different characteristics. Finally, the identification method proposed in this paper shortens the time by 85.71% compared with the identification method of data collection, and shortens the identification time by 83.33% compared with the IoT identification method. This greatly improves the recognition of different musical feature types.

Music Feature Recognition and Classification Using a Deep Learning Algorithm

This paper studied music feature recognition and classification. First, the common signal features were analyzed, and the signal pre-processing method was introduced. Then, the Mel–Phon coefficient (MPC) was proposed as a feature for subsequent recognition and classification. The deep belief network (DBN) model was applied and improved by the gray wolf optimization (GWO) algorithm to get the GWO–DBN model. The experiments were conducted on GTZAN and free music archive (FMA) datasets. It was found that the best hidden-layer structure of DBN was 1440-960-480-300. Compared with machine learning methods such as decision trees, the DBN model had better classification performance in recognizing and classifying music types. The classification accuracy of the GWO–DBN model reached 75.67%. The experimental results demonstrate the reliability of the GWO–DBN model. The GWO–DBN model can be further promoted and applied in actual music research.

한류체험프로그램으로서 외국인 한국무용 교육에 대한 요구도

The purpose of this study is to examine the needs of foreigners" Korean dance education as a Hallyu experience program. As a Hallyu experience program, a qualitative research method was applied to investigate the demand for Korean dance education for foreigners. Specifically, the educational requirements were examined through in-depth interviews with participants participating in the foreign Korean dance education program as of September 2022. Specific research results are as follows. First, the teaching method needs were divided into teaching methods, music types, and video shooting, and detailed demonstrations, repetitive learning, kindness, continuous passion, Korean traditional music use, collection value, and reference video shooting were investigated. Second, as for the contents of Korean dance education, opinions were surveyed that they wanted to learn fan dance, sword dance, and Ganggangsuwolae works only after class, use small tools, and learn more about humanities. Third, after participating in the fan dance program, it was analyzed that the attractiveness of fan dance increased, the image of Korean culture increased, and the interest in traditional culture increased.

Research on pattern recognition of different music types in the context of AI with the help of multimedia information processing

Music is a form of art in which the sounds are timed and organized. Music is a kind of entertainment that mixes sounds in a way that people like, find fascinating, or to which they desire to dance. Most music is created via one or more people's vocal or instrumental efforts. By the dictionary, music is defined as having at least one of the following three elements: rhythm, melody, and harmony. Music is utilized in therapy because of its apparent benefits on behavior. Various physiological circumstances have different metabolite expression patterns that can be studied using pattern recognition in multimedia information processing. Music therapy includes various activities, including singing, playing instruments, dancing, and listening to music. Music-making with artificial intelligence (AI) uses neural networks, which are massive collections of computer bits that aim to stimulate brain activity. The neural network (NN) may be bombarded with music to see if it picks up on patterns the way the human brain does when repeatedly exposed to new stimuli. It will get the hang of them eventually. Experts believed that AI would be unable to generate music unless it first mimics a human-created data collection. By providing a conceptual paradigm for multimedia information processing. The end effect will be entirely different depending on how many hours of music are placed into it. For AI to learn from patterns or features in data on its own, it needs big data (BD), fast, repeated processing, and complex algorithms. The use of technology makes the process of creating analytical models much faster. A new AI-BD tool is an opportunity, not a danger for people currently working as artists. People are beginning to ask what constitutes acceptable work as AI grows more prominent in the music and art industries to gain efficiency of 97.8%. Future music will be heavily impacted by listeners' bodies and emotions all the time. For example, wearable technology may detect a person's mood and play the music that matches it. It is the next step in personalization. The AI-BD methodology improves the efficiency, accuracy, etc., compared to other existing models by gaining 97.8%, performance analysis 97.2%, reliability ratio 95.6%, and survivability analysis 98.2%.

The Use of Cymatics: Changing Face of Music Teaching and Learning in Ghanaian Schools

Functionally, Music and Science have a lot in common to offer humanity and are largely inseparable. However, very little attention is paid to an exploration that would expose music educators to the complementarity of music and science in their pedagogical approaches to music learning. The paper, therefore, focuses on the use of science in music to initiate innovation in music education through Cymatics. Using the experimental approach and Cymatic setup, the three main Ghanaian musical types and randomisation and five songs were selected from a collection of twenty songs based on the three musical types to generate cymatic figures which were induced by frequencies and amplitudes. It was observed that the figures created from the three Ghanaian musical types possessed some similarities, yet they uniquely stood out as a brand. It was also detected that melodies produced more figures than harmonised songs. The consumable sound levels (20 to 20000 Hz) were found to produce legible figures for use in other ventures. The study recommends that Music educators should record and use the generated frequencies and amplitudes for replication to aid comprehension in the teaching and learning process. Keywords: Cymatics, music teaching, science and music education

A Descriptive Survey of Atmospheric Music Broadcasting in Hot Spring Hotels and Its Further Marketing Strategy

The survey purposes are to preliminarily understand the current situation of atmospheric music broadcast in hot spring hotels and why these choices of music were made. We reviewed literatures regarding applied music and music types, and applied questionnaire, participant observation and unstructured interviews with hotel staff who broadcast music in the unique, famous and historic Guanzihling hot spring region of Taiwan. Results show that music is often broadcast in public areas, such as lobby and restaurants. On the contrary, there was no music broadcast in private areas, such as guest rooms and private thermal baths. Light music is the primary music choice. Music types is chosen without rules by staff who have no basic trainings in music. Possible marketing strategies for future studies include the staff trainings and construction of hotel’s own hot spring music database, and better marketing of hotel’s unique multi-sensory brand image through its distinctive and pleasant music.

Exploring the Preferences of US YouTube Users and Factors Related to YouTube Uploader’s Revenue

YouTube is a mainstream media that is popular with the general public. It has a powerful yet special force that attracts almost all young people who come into contact with it. YouTube permeates every aspect of people’s lives, education, daily life, work, leisure, and entertainment. Everyone can be a professional uploader. It is very important to know how to be a good uploader and what the important elements are related to generating revenue from YouTube. This article uses the top 1000 channels and the top 200 daily YouTube video’s data from September 2020 to January 2022 in the United States to analyze popular categories of channels, and use the YouTube videos and their related data from September 2020 to January 2022 from Ken Jee to analyze uploader characteristics. The dataset of this article is based on YouTube API and the uploader’s release. CRISP-DM method is applicated to exploring the data in Jupyter using Python. In addition, Pearson correlation analysis is used to analyze revenue-related metrics. This paper contains both qualitative and quantitative analyses. In this paper, we found that among the top 1000 most popular uploaders, Entertainment, and Music types are the most popular, with 241 and 222 respectively. The Autos & Vehicles and Travel & Events categories are the least represented, with only 1 on the list. This article also finds out Entertainment, Music, People & Blogs and Gaming type of videos users are most attracted to, the core audience, and their interests. Have strong positive relation with revenue. In addition, watch time (hours), subscribers lost, likes, shares, and dislikes are strongly positively correlated with revenues. Comments are moderately positively correlated with revenues. RPM and CPM almost have no correlation with revenue. This study offers a statistical analysis of the content genre based on a number of categories and details of YouTube users’ interests. This study also demonstrates the variables that might have an impact on YouTube uploaders’ revenue. So that uploaders, researchers, and investors can acquire a rough idea of the impact on YouTube.

Chinese theories of “mud” music and poetry

The article is focused on analysis of ideas of human creative activities in its connections with the spiritual life of society and state board system, which were among the fundamental problems of the Ancient Chinese thought, begging with its originative stage (mid 1st cent. BCE). The problem received a particular development in Confucian thought, and initially was applied to musical types of art (“music-Yue”), endowed with special metaphysical properties. Going from the analysis of original theoretical works the fact of formation of the holistic concept of “mud music” (yin yue) is argued. As “mud” is offered with a certain proportion of conditionality to translate the character yin, originally designated the river flow released from the coast and mixed with mud and, later, turned into a categorical term, which transmitted the totality of human Vices and related actions: immorality (including sexual behavior), debauchery, etc., and which is usually rendered in English as “excessive, licentious, obscene”. It is stated, that the spread of “mud music”, born by ethnical (barbarian) musical folklore, within the native Chinese social and cultural compound was considered by the Chinese thinkers to be the embodiment of its spiritual degradation, starting with the ruling elite. The growing popularity of “mud music” aggravated the further fall of the public moral foundations, inevitably leading to the decease of the state. At the end of this millennium, the ideas of “mud music” were transferred to poetic creativity. As a result, it is assumed that the Chinese philosophy and aesthetic thought were originally inherent in the notions of creative activity, expressing the most Vile human qualities and aspirations, and which was endowed with the ability to render debasing impact on the individual, human society and the world order.

EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare.

Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions. Our proposed EER model (EEG-based Emotion Recognition Model) could identify 20 types of emotions based on 32 EEG channels, and the average recognition accuracy was above 90% and 80%, respectively. Our proposed music-based emotion classification model (MEC model) could classify eight typical emotion types of music based on nine music feature combinations, and the average classification accuracy was above 90%. In addition, the semantic mapping was analyzed according to the influence of different music types on emotional changes from different perspectives based on the two models, and the results showed that the joy type of music video could improve fear, disgust, mania, and trust emotions into surprise or intimacy emotions, while the sad type of music video could reduce intimacy to the fear emotion.

Meta-analysis of the interventional effects of music therapy on pain and anxiety of burn patients in wound dressing change

Objective: To evaluate the interventional effects of music therapy on pain and anxiety of burn patients in wound dressing change. Methods: The meta-analysis method was adopted. Databases including China National Knowledge Internet, Wanfang Database, and VIP database were retrieved with the search terms in Chinese version of ", , /, /", and PubMed, Embase, and Cochrane Library were retrieved with the search terms in English version of "music, burn, dressing change/wound dressing, pain/ache/sore" to obtain the publicly published randomized controlled trials on the application of music therapy for wound dressing change in burn patients from the establishment of each database to May 2021. The outcome indexes included pain score/percentage and anxiety score after dressing change. Rev Man 5.4 and Stata 14.0 statistical software were used to conduct a meta-analysis of eligible studies. Results: A total of 520 burn patients from 7 studies were included, including 260 patients in music therapy group who received music therapy and 260 patients in routine dressing change group who received routine dressing change. The bias risk of all the 7 included studies was uncertain. Compared with those in routine dressing change group, the pain percentages (relative risk=0.06, 95% confidence interval=0.01-0.41, P<0.01) and pain scores after dressing change (standardized mean difference (SMD)=-0.91, 95% confidence interval=-1.61--0.22, P<0.05) of patients in music therapy group were significantly lower. Subgroup analysis showed that music type and timing of intervention might be the source of heterogeneity in pain scores after dressing change. The anxiety scores of patients in music therapy group were significantly lower than those in routine dressing change group (SMD=-0.64, 95% confidence interval=-1.09--0.19, P<0.01). There was no publication bias in pain or anxiety scores after dressing change. Conclusions: Music therapy can relieve the pain and anxiety of burn patients during dressing change.

Classification System of National Music Rhythm Spectrogram Based on Biological Neural Network.

National music is a treasure of Chinese traditional culture. It contains the cultural characteristics of various regions and reflects the core value of Chinese traditional culture. Classification technology classifies a large number of unorganized drama documents, which are not labeled, and to some extent, it helps folk music better enter the lives of ordinary people. Simulate folk music of different spectrum and record corresponding music audio under laboratory conditions Through Fourier transform and other methods, music audio is converted into spectrogram, and a total of 2608 two-dimensional spectrogram images are obtained as datasets. The sonogram dataset is imported into the deep convolution neural network GoogLeNet for music type recognition, and the test accuracy is 99.6%. In addition, the parallel GoogLeNet technology based on inverse autoregressive flow is used. The unique improvement is that acoustic features can be quickly converted into corresponding speech time-domain waveforms, reaching the real-time level, improving the efficiency of model training and loading, and outputting speech with higher naturalness. In order to further prove the reliability of the experimental results, the spectrogram datasets are imported into Resnet18 and Shufflenet for training, and the test accuracy of 99.2% is obtained. The results show that this method can effectively classify and recognize music. The experimental results show that this scheme can achieve more accurate classification. The research realizes the recognition of national music through deep learning spectrogram classification for the first time, which is an intelligent and fast new method of classification and recognition.

Adjustable acoustics for imperfect rooms

Very often, acousticians are faced with room dimensions that are fixed and cannot be adjusted. This dilemma is exascerbated when the room is to be used for purposes of music performance. It is further complicated when the goal of the room is to present a broad spectrum of music types from classical symphony to modern jazz or rock. An example will be presented of an essentially square room space that was designed and treated to be adaptable to meet the requirements of all types of performances. Tabor College in Hillsboro, KS had space on its campus to build one concert that had to serve all of its performance needs. The physical space available, in order to maximize the size of the hall, also turned out to be a nearly perfect square in the audience area. Tom Ryan of The Hallani Group, working with the author and RealAcoustix, designed an acoustical system that allows the room to perform well for all types of music performances while being easy to change and architecturally beautiful. The design criteria, auralizations, and other information will be presented showing how this was accomplished.

Findings on the Musical Identity of Âşık Yaşar Reyhanî, Cultural Ambassador of Minstrel Tradition

Âşıklar, Türk kültürünün hâkim olduğu coğrafyalarda sözlü gelenekle beraber müzik kültürünü de temsil ve muhafaza etmede önemli bir noktadadır. Sosyo-kültürel yapıya yön vererek toplumun sanatsal ifade ve algısının gelenek ve görenekler içerisinde kalmasını sağlamıştır. Âşık Reyhanî, gelenek bağlılığında âşık edebiyatını ve müziğini bir sonraki nesle aktarma hususunda yetiştirdiği çıraklarla, topluma kazandırdığı usta malı ve kendi ürettiği eserlerle kendinden önceki ve sonraki nesil arasında köprü olmayı başarmış bir âşıktır. Bu çalışmanın amacı, Âşık Reyhanî’nin gerek kuramsal bilgileri ve zengin melodi dağarıyla, gerekse de ses ve saz icra teknikleriyle âşık müziğine olan hâkimiyetini bilgi ve belgeler ışığında ortaya koymaktır. Araştırmanın kuramsal bölümü oluşturma adına; ozan, âşık, âşıklık geleneği, Türk halk müziğinde âşıklık geleneğinin yeri, Erzurum ili âşık müziği ve Âşık Reyhani’nin sanat yaşamına yönelik literatür taraması yapılmıştır. Âşık Reyhanî’ye ait müzikal bulguları elde etme adına verilerin toplanmasında belgesel tarama modeli kullanılarak Âşık Reyhanî’nin plak-kaset albümleri, video kayıtları, katıldığı televizyon programları, Âşık Reyhanî temalı belgeseller ve yazılı kaynaklardan faydalanılmıştır. Elde edilen bulgulara doğrultusunda; Âşık Reyhanî bağlama sazının icrasına yönelik çeşitli icra tekniğine, tel ve perde düzeni bilgilerine sahiptir. İcralarında ekseriyetle divan sazını kullanmakla beraber tambura sazını da tercih ettiği görülmektedir. Âşık Reyhanî albümlerinde Maya ve Divan gibi Türk halk müziği uzun hava türlerine benzerlik gösteren uzun havalar; Sümmanî ve Köroğlu havaları gibi çeşitli ezgi kalıplarında kırık havalar seslendirmiştir. Âşık Reyhanî’nin seslendirdiği eserlerin diyapazona göre karar sesleri incelendiğinde genel olarak fa# (F#3), fa (F3), ve sol (G3) sesleriyle söylediği görülmektedir. İcra ettiği eserler ekseriyetle bir oktavı aşmayacak şekilde olup 4-5 ve 7 ses aralığındadır. Yörede sık kullanılan Uşşak ve Hüseyni makamlarının dışında Hicaz, Muhayyer, Saba ve Karcığar makamlarıyla eserler de seslendirmiştir. Âşık Reyhanî kırık havaları en çok 4/4 ve 2/4’lük basit usulde ve 5/8’lik bileşik usulde icra etmiştir.

ELAZIĞ YÖRESİNDEN BİR SALÂT ÖRNEĞİ: UŞŞAK SALÂTÜSELÂM

Arapçada hayır dua içeren pekçok kavramı ihtiva eden salâtı kısaca; “Hz. Muhammed’e (s.a.v.) merhamet, mağfiret, feyz ü bereket vs. hayırlar ihsan etmesini Allah’tan (c.c.) dilemek” şeklinde tanımlamak mümkündür. Dinî naslar (âyet ve hadisler) icabı her müslümanın söylemesi gereken salât metinlerini mûsikî eşliğinde okumak, dinî Türk mûsikîsinde “salât” türünü meydana getirmektedir. Müslüman Anadolu insanının en fazla rağbet ettiği dinî Türk mûsikîsi türlerinden biri olan salât, “salâtüselâm” veya “salavat/salavat-ı şerife” olarak da adlanlandırılmış, ibadet mekânları olan câmilerde ve tasavvufî düşüncenin pratiğe dönüştüğü yerler olan tekkelerde büyük bir iştiyakla icra edilmiş, insan hayatının hemen her önemli safhasında teberrüken okunmuştur. Bu türe gösterilen büyük rağbet, pek çok salâtın ortaya çıkmasına vesile olmuş, bunlardan bir kısmı kayıt altına alınarak günümüze gelebilmiş, ancak bir kısmı da kayıt altına alınamadığından dolayı maalesef unutulmuştur. Ancak Anadolunun birçok yöresinde geleneksel olarak varlığını sürdürmesine rağmen henüz kaydedilmemiş salâtlar da vardır ve bunlardan biri de Elazığ yöresinde icra edilen Uşşak makamındaki “salâtüselâm” adlı eserdir. Çalışmamızın konusunu da oluşturan bu eser, bölgedeki tasavvuf çevreleri (özellikle de Kâdirîler) tarafından okunmaktadır.

Comparative analysis of Chinese and European classical music

The purpose of the present research is to conduct a quantitative analysis of Chinese classical music and then compare it with Western European classical music. The results obtained under the comparative analysis of samples using the Student's t-test for independent variables have shown that there is a significant difference between the studied music types. In this case, the present study presupposed analyzing a total number of 18 musical scores. Mathematical analysis facilitated forming 12 samples from independent variables that characterized the length of the studied excerpts, compassed the number of repetitions, the initial interval, and the number of unique intervals. Despite a significant contribution of European classical music to Chinese music, it suggests that Western European music is more complex than Chinese classical music. Accordingly, the results obtained can be useful in the field of musicology and music theory.

Who Takes Music With Them When They Transition to High School?

According to music educators, persistence beyond a student’s initial enrollment in middle and high school music is a problem; however, there has been little research to substantiate this claim. Although several recent large-scale, longitudinal studies of initial selection into music classes have been conducted, longitudinal studies on who persists (vs. quits) in music—especially from middle to high school—are overdue. We prospectively followed a large ( n = 3,393), ethnically diverse (62% Hispanic, 29% Black), predominately low-income (77% free/reduced-price lunch) sample of eighth-grade middle school music students to high school (ninth grade) to understand predictors of persistence. Overall, only 24.5% of students taking a music elective in eighth grade continued to do so in ninth grade (band = 20.4%, chorus = 21.8%, guitar = 12.3%, orchestra = 20.4%). Initially more academically competent students (higher eighth-grade grade point average and reading and math scores) and students with disabilities were more likely to persist with music from eighth to ninth grade. Predictors varied somewhat by music type. A multigroup analysis showed moderation across music types with respect to the effect of gender, gifted status, and math on music persistence (e.g., high math scores predict band but did not predict other music-type persistence). Implications for music educators and researchers are discussed.

The Experience of Music Perception: A Review of Physiological, Neural, and Psychological Responses

Music allows artists to express and connect themselves with broad and diverse audiences. Certain musical choices can greatly impact the amount of influence the artists’ message has on an audience. To best share messages through a musical work, it can be beneficial for an artist to understand the biological processes with which the audience may experience the perception of music. The physiological, psychological, and neural responses represent the bodily effects of music and are thus informative to study in relation to the individual experience of a musical work on an individual. This paper will review the biological view of music and create a narrative to show how physiology, psychology, and neurology can be applied to influence a social or individual atmosphere toward an artist's desired message. After synthesizing the biological effects of musical feature changes (such as tempo, rhythm, volume, pleasantness, structure, music type, emotions, and audience type) this paper will make three conclusions relating to the intersection of physiology, neurology, and psychology. First, the ability to prefer or expect a musical change or feature can have a large emotional impact. Second, the arguably most important musical emotion of empathy can likely be created through rhythmic features of tempo and synchronization. Third, peak musical experiences are emotionally arousing and largely influenced by musical structure and audience setting.

Effects of Gender and Music Genre on Hand-Eye Coordination Task among a Private University Students in Nigeria

Music has been described as an enhancer of task performance and this varies according to the genre of the music, however, influence of music type on performance, especially in native language has been equivocal. The present study investigated the influence of music genre and gender on eye-hand coordination task performance among private university. Using 144 randomly selected fresh undergraduates who were assigned to a 2x3 design groups, working on O’Connor finger dexterity test, it was observed that performance was generally low. There was marginal better performance of females than males especially while listening to Gospel music, while males did better while listening to local Fuji music. There was no significant main effect of genre and gender on the task performance. It was concluded that performance of females while listening to gospel music may be characteristic of their nature especially when they performance house chores in Nigeria.

Active Learning Music Genre Classification Based on Support Vector Machine

The improved SVM (support vector machine) offers an active training method that provides users with the most informative sample through multiple iterations and adds it to the training package, which can significantly reduce the cost of manually labeling samples. To evaluate the classifier’s performance, 801 music samples were tested for five music types (Dance, Lyric, Jazz, Folk, and Rock). The effectiveness of the proposed SVM active training method was confirmed by two things: the convergence speed and the classification accuracy, and the number of samples to be labeled with the same accuracy. And the classification accuracy was 81%. At the expense of a little precision, both SVM active training methods drastically reduce the number of labels to be trained, and the method proposed in this paper works better. At the same time, the smaller the value, the fewer the labels that need to be labeled. This is because increasing the number of iterations allows the classifier to select the most appropriate sampling points, while the larger the set value, the smaller the number of iterations. So you can choose between the two depending on the actual situation.