Music Files Research Articles

Acapella-based music generation with sequential models utilizing discrete cosine transform

Making musical instruments that accompany vocals in a song depends on the mood quality and the music composer’s creativity. The model created by other researchers has restrictions that include being limited to musical instrument digital interface files and relying on recurrent neural networks (RNN) or Transformers for the recursive generation of musical notes. This research offers the world’s first model capable of automatically generating musical instruments accompanying human vocal sounds. The model we created is divided into three types of sound input: short input, combed input, and frequency sound based on the discrete cosine transform (DCT). By combining the sequential models such as Autoencoder and gated recurrent unit (GRU) models, we will evaluate the performance of the resulting model in terms of loss and creativity. The best model has a performance evaluation that resulted in an average loss of 0.02993620155. The hearing test results from the sound output produced in the frequency range 0-1,600 Hertz can be heard clearly, and the tones are quite harmonious. The model has the potential to be further developed in future research in the field of sound processing.

DNA-DISK: Automated end-to-end data storage via enzymatic single-nucleotide DNA synthesis and sequencing on digital microfluidics

In the age of information explosion, the exponential growth of digital data far exceeds the capacity of current mainstream storage media. DNA is emerging as a promising alternative due to its higher storage density, longer retention time, and lower power consumption. To date, commercially mature DNA synthesis and sequencing technologies allow for writing and reading of information on DNA with customization and convenience at the research level. However, under the disconnected and nonspecialized mode, DNA data storage encounters practical challenges, including susceptibility to errors, long storage latency, resource-intensive requirements, and elevated information security risks. Herein, we introduce a platform named DNA-DISK that seamlessly streamlined DNA synthesis, storage, and sequencing on digital microfluidics coupled with a tabletop device for automated end-to-end information storage. The single-nucleotide enzymatic DNA synthesis with biocapping strategy is utilized, offering an ecofriendly and cost-effective approach for data writing. A DNA encapsulation using thermo-responsive agarose is developed for on-chip solidification, not only eliminating data clutter but also preventing DNA degradation. Pyrosequencing is employed for in situ and accurate data reading. As a proof of concept, DNA-DISK successfully stored and retrieved a musical sheet file (228 bits) with lower write-to-read latency (4.4 min of latency per bit) as well as superior automation compared to other platforms, demonstrating its potential to evolve into a DNA Hard Disk Drive in the future.

Music Genre Classification

The Music Genre Classification model automatically divides music into different genres using a small number of audio files and a range of musical attributes. This topic is highly relevant to the field of music information retrieval since it provides a way to organize and analyze large amounts of music files. For MGC, standard machine learning techniques such as SVM, KNN, Decision trees, and neural networks can be applied. These algorithms are trained to recognize different musical qualities and traits, which allows them to categorize the audio files into different genres. Numerous applications show that deep learning algorithms—such as CNN, ANN, and others—perform better than conventional machine learning algorithms. Consequently, the CNN method is adjusted to perform the categorization of music files. This classifies musical genres using deep learning methods from CNN. To evaluate the effectiveness of the MGC algorithms, accuracy is used. Moreover, the impact of different algorithms on MGC performance can be compared and studied. It can be applied to automated music recommendation systems, music production, and music education.

Classification of music genre using support vector machine and convolutional neural network

Music is an art of combining rhythms, melodies, harmony, and form and also an art of expressing content. Almost all people in this world love Music. This depends on the person and what type of Music he likes to listen to. Either it will be Classical, melodious rock Hip-hop, or some other class of genre. The task of classifying music files based on genre is extremely difficult, especially in the area of MIR (Music Information Retrieval). The approach involves deep learning, where CNN and SVM models are trained end-to-end using a spectrogram to classify a signal's genre label. Every person has a different choice. In this paper, a set of Music is classified into different types of genres with the help of machine learning. A set of 1000 audio files containing 10 types of genres, with 100 audio files each has been taken. The audio features have been extracted using the Spectral roll-off, Chroma features, and zero Crossing Rate. Then, the model was predicted using different algorithms like Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). After the model is predicted using the algorithms, CNN outperformed SVM and gave an accuracy of 96.2%.

Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion

Abstract In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction and emotional expressiveness—have garnered increasing scholarly attention. This research leverages MIDI music file formats to mine necessary data, engaging in a detailed examination of the structural and characteristic elements of MIDI files. To optimize the precision of mathematical models applied in this study, MIDI music data underwent preliminary preprocessing steps, including hexadecimal encoding and feature filtering, which facilitated the transformation of MIDI features into vectors representing emotional attributes. The study introduced the development of a BiLSTM_BLS model tailored to articulate the nuances of piano performance and emotional expression. This model synthesis integrates features reflective of emotional states induced by piano playing. A mathematically derived loss function was utilized to gauge the model’s fidelity, thereby enabling an assessment of its efficacy in the realms of artistic instruction and emotional conveyance in piano performance. Findings indicate a proximity between the emotional states of Anger (0.392) and Stress (0.385), with Depressed (0.422) presenting a slightly elevated distance from these states. Moreover, the BiLSTM_BLS model achieved a performance score of 3.214 in the evaluation of artistic expression, which corresponds to 64.28 on a standardized 100-point scale. This investigation contributes to the enhancement of musical skills and theoretical knowledge among pianists, promotes cognitive development, augments emotional expression, and nurtures creativity.

THE SYNERGY OF ART AND TECHNICS: DEVELOPMENT DIRECTIONS OF MUSICAL INFORMATION TECHNOLOGIES

This article explores contemporary trends in the development of music information technologies, which refinement constitutes a key factor in the advancement of the music industry. The paper analyzes the advantages and challenges associated with these trends, while also forecasting potential directions for the future development of music information technologies. With an interdisciplinary approach at the intersection of musical art and information technologies, the research highlights the impact of modern technologies, the evolution of microelectronics, computers, and artificial intelligence on music creation and music education. Specifically, the transformation processes of physical sound devices into a virtual dimension are examined. The authors propose a definition of the concept of music information technologies as a symbiosis of information technologies and musical art. In other words, music information technologies are information technologies that address, work on, or influence processes within the field of musical art. The authors conclude that studying the issues related to the functioning of music society created through music information technologies is a promising direction in musicology, music education, and related fields. Based on the analysis of Music Information Technologies trends, it is noted that manufacturers of musical equipment and software developers are gradually transitioning to electronic versions of documentation and video tutorials. Trends include an increase in the number of music file formats, higher sampling frequencies, and the resolution of musical digital systems. The authors highlight the development of specialized digital workstations and other software compatible with general standards but specialized for specific individual physical devices or controllers. The article also anticipates the creation of new methods for controlling musical instruments and devices, widespread use of cloud technologies, and artificial intelligence.

Multidimensional Scale Analysis and Modeling of Emotional Space in Yao Music

Abstract The research content of this paper gives a detailed analysis of the system structure of the music emotion recognition system. In the process of music feature extraction and recognition, the system first reads the note queue and tempo information queue of the format music and then analyzes and calculates the basic features of the music, tonal and melodic features, etc., respectively. The content of the text set is projected into a three-dimensional visual space using the multidimensional scale analysis method, and then the emotional theme is obtained by dividing the class groups. The samples from the Yao music database are selected as the data source for music emotion research, and performance analysis is performed. The results prove that the method of this paper can effectively perform feature extraction on Yao music files, with an average misdetection rate of only 0.09% and a recognition rate of more than 83% in all cases. The musical structure of Yao Village Impression can be broken down into five parts. The musical melodies mostly consist of major second and minor third intervals. The emotional labeling of structures 1 and 4 in the cluster analysis is distributed in four quadrants, with a preference for pleasurable emotions. Structure 2 was more concentrated in quadrants 1, 4. These emotion distribution results are consistent with the music depicting the beauty of Yao village and the emotional life of Yao people.

Emotion recognition from MIDI musical file using Enhanced Residual Gated Recurrent Unit architecture

The complex synthesis of emotions seen in music is meticulously composed using a wide range of aural components. Given the expanding soundscape and abundance of online music resources, creating a music recommendation system is significant. The area of music file emotion recognition is particularly fascinating. The RGRU (Enhanced Residual Gated Recurrent Unit), a complex architecture, is used in our study to look at MIDI (Musical Instrument and Digital Interface) compositions for detecting emotions. This involves extracting diverse features from the MIDI dataset, encompassing harmony, rhythm, dynamics, and statistical attributes. These extracted features subsequently serve as input to our emotion recognition model for emotion detection. We use an improved RGRU version to identify emotions and the Adaptive Red Fox Algorithm (ARFA) to optimize the RGRU hyperparameters. Our suggested model offers a sophisticated classification framework that effectively divides emotional content into four separate quadrants: positive-high, positive-low, negative-high, and negative-low. The Python programming environment is used to implement our suggested approach. We use the EMOPIA dataset to compare its performance to the traditional approach and assess its effectiveness experimentally. The trial results show better performance compared to traditional methods, with higher accuracy, recall, F-measure, and precision.

A Music File Detection Method based on Convolutional Neural Network for Video-on-Demand Platform

On the video-on-demand (VOD) platform, audio processing includes noise suppression, loudness normalization, codec and others. However, these processing are not suitable for all scenes, especially for music audio quality and subjective perceptual. Therefore, different processing will be selected according to the music detector result. In this paper, a music or non-music audio detection used convolution neural network based on the Mel-spectrogram and modified spectral flux with music frame proportion estimation method is proposed. In the method, the Mel-spectrogram flux will greatly improve the traditional spectral flux calculation. This paper creatively proposes a music file-wise detection algorithm based on the VOD platform compared with previous frame-wise detection method. Proposed method has strong robustness, high computing efficiency, and low delay. It is more suitable for VOD platform audio detection requirements, and has direct benefits for audio quality control.

Deep learning Binary/Multi classification for music's brainwave entrainment beats.

The last two decades have seen the emergence of a brand-new kind of music known as digital brain stimulant, also known as instrumental music or music without lyrics, which mostly comprises entrainment beats. While listening to it has the same ability to affect the brain as taking medication, it also has the risk of having a negative impact or encouraging unwanted behavior. This sparked the interest of a large number of studies in the psychological and physiological effects of music's brainwave entrainment beats on listeners. These studies started to categorize and examine how musical beats affected brainwave entrainment by looking at electroencephalogram (EEG) signals. Although this categorization represents a step forward for the early research efforts, it is constrained by the difficulty of having each musical track and conducting EEG tests on humans exposed to distortion due to noise in order to determine its influence. The work proposed in this article continues to explore this topic but in a novel, simple, accurate, and reliable categorization procedure based on the music signal elements themselves rather than dependent on EEG. VGGish and YAMNET based transfer deep learning models, are tuned to handle a straightforward, accurate real-time detector for the existence of the music beats inside music files with accuracy of 98.5 and 98.4, respectively. Despite the fact that they yield results that are equivalent, the YAMNET model is more suited for use with mobile devices due to its low power consumption and low latency. The article also proposes modified version of VGGish and YAMNET binary classifying models called BW-VGGish and BW-YAMNET respectively. The modification was to turn the binary classification into multi-classification. These multi-classifiers handle the classification of the influence of music beats (five different brain waves) on human brainwave entrainment with average accuracy of 94.5% and 94.5%, respectively. Since there was a lack of datasets addressing this kind of music, two datasets, the Brainwave Entrainment Beats (BWEB) dataset and the Brainwave Music Manipulation (BWMM) dataset, were generated for classification training and testing. The re-testing on a sample of music files that have their impact on brain waves (with their EEG) in an earlier study is done to strengthen the validity of the proposed work and to overcome the potential limitation of utilizing a music dataset that is not proved with its EEG. The success of the suggested models was demonstrated.

A Comprehensive Review on Music Transcription

Music transcription is the process of transforming recorded sound of musical performances into symbolic representations such as sheet music or MIDI files. Extensive research and development have been carried out in the field of music transcription and technology. This comprehensive review paper surveys the diverse methodologies, techniques, and advancements that have shaped the landscape of music transcription. The paper outlines the significance of music transcription in preserving, analyzing, and disseminating musical compositions across various genres and cultures. It also provides a historical perspective by tracing the evolution of music transcription from traditional manual methods to modern automated approaches. It also highlights the challenges in transcription posed by complex singing techniques, variations in instrumentation, ambiguity in pitch, tempo changes, rhythm, and dynamics. The review also categorizes four different types of transcription techniques, frame-level, note-level, stream-level, and notation-level, discussing their strengths and limitations. It also encompasses the various research domains of music transcription from general melody extraction to vocal melody, note-level monophonic to polyphonic vocal transcription, single-instrument to multi-instrument transcription, and multi-pitch estimation. The survey further covers a broad spectrum of music transcription applications in music production and creation. It also reviews state-of-the-art open-source as well as commercial music transcription tools for pitch estimation, onset and offset detection, general melody detection, and vocal melody detection. In addition, it also encompasses the currently available python libraries that can be used for music transcription. Furthermore, the review highlights the various open-source benchmark datasets for different areas of music transcription. It also provides a wide range of references supporting the historical context, theoretical frameworks, and foundational concepts to help readers understand the background of music transcription and the context of our paper.

Методика застосування інноваційних технологій навчання в диригентсько-хоровій підготовці майбутніх учителів музичного мистецтва

The purpose of the article is to consider the extent to which innovative tech­nologies are in demand in higher education institutions in the context of studying conducting and choral disciplines and to determine their feasibil­ity, effectiveness, usefulness and prospects for use in the training of future choirmasters. The authors determine the necessity and expediency of form­ing an innovative space for a number of disciplines based on the use of achievements of communication technologies. They reveal the effective­ness and efficiency, usefulness and prospects of their use in the process of training choral conductors. The introduction of innovative technologies in educational practice is con­ditioned by the fact that it should serve as a means of solving some urgent problems of a certain University and pass experimental testing for its final implementation. One of the types of innovative technologies in such classes is the use of vir­tual electronic music choral anthology, which is systematized in the form of a table with URL to the music medium, sheet music and text files that are freely available online. Another innovation in the study of the cycle of conducting and choral disciplines is the creation of an intellectual map. This method is appropriate when studying the theory of conducting (its technical component, artistic and expressive, communicative and psychological, man­agerial and organizational). Along with intelligence maps, there are also so-called web quests, which are aimed at expanding the knowledge of concepts and terms related to understanding the specifics of conducting activities. Distance learning encourages teachers of almost all specialties, including conducting and choral specialties, to introduce such a form of work as a webinar. Often in the process of studying conducting and choral disciplines, students undergo so-called socio-psychological training. Practice shows that today the use of some of the latest technologies in the context of studying conducting and choral disciplines in higher education not only stimulates the activity of students in the educational process, but also gives it an innovative appeal. At the same time, they contribute to the creation of a creative atmosphere in the seminar-type classes, provide an opportunity for self-realization of creative potential when students perform independent work, thereby improving the quality of learning material.

Evaluation of the Effect of Maternal Education on Toddler Dental Plaque Removal Based on a Health Belief Model.

Objectives: The objective of this study was to investigate the impact of an educational intervention, guided by the Health Belief Model (HBM), on promoting preventive behaviors among mothers to combat early childhood caries (ECC) in toddlers aged 12 to 18 months. Materials and Methods: This single-blind interventional study evaluated 92 toddlers between 12 to 18 months and their mothers, who were selected by stratified random sampling. Data regarding demographic and nutritional factors were collected using a questionnaire. The dental status of children was evaluated by clinical examination and measuring the plaque index (PI) of maxillary central incisors and the dmfs index. A pamphlet designed according to the HBM was used for oral hygiene instruction in both the intervention and control groups. All of the mothers in the intervention group received a music file to listen to when brushing their children's teeth. Data were analyzed using chi-square, Fisher exact, Mann-Whitney, and Wilcoxson tests. P<0.05 was considered significant. Results: No significant correlation was noted between the daily frequency of sugary substance intake and primary PI in the intervention or control group with/without dental plaque (P>0.05). In the intervention group, PI significantly decreased after the intervention compared to baseline (P<0.001). However, there was no significant change in PI after the intervention in the control group compared with baseline (P=0.1). Conclusion: The findings indicate that a combination of educational intervention and incentives, such as using age-appropriate music for toddlers, can significantly enhance mothers' effectiveness in removing dental plaque.

An adaptive control framework based multi-modal information-driven dance composition model for musical robots.

Currently, most robot dances are pre-compiled, the requirement of manual adjustment of relevant parameters and meta-action to change the dancing to another type of music would greatly reduce its function. To overcome the gap, this study proposed a dance composition model for mobile robots based on multimodal information. The model consists of three parts. (1) Extraction of multimodal information. The temporal structure feature method of structure analysis framework is used to divide audio music files into music structures; then, a hierarchical emotion detection framework is used to extract information (rhythm, emotion, tension, etc.) for each segmented music structure; calculating the safety of the current car and surrounding objects in motion; finally, extracting the stage color of the robot's location, corresponding to the relevant atmosphere emotions. (2) Initialize the dance library. Dance composition is divided into four categories based on the classification of music emotions; in addition, each type of dance composition is divided into skilled composition and general dance composition. (3) The total path length can be obtained by combining multimodal information based on different emotions, initial speeds, and music structure periods; then, target point planning can be carried out based on the specific dance composition selected. An adaptive control framework based on the Cerebellar Model Articulation Controller (CMAC) and compensation controllers is used to track the target point trajectory, and finally, the selected dance composition is formed. Mobile robot dance composition provides a new method and concept for humanoid robot dance composition.

Capital Commodification in the Field of Tarling Music Digitalization

Tarling’s method for creating Tarling music is evolving over time. The digitalization field observes the agents’ ways in production, distribution, and consumption of digital music files. Agents build their own habitus in employing their numerous capitals in the digitalization space. By combining the correlation of habitus and field theories of Bourdieu and Mosco’s perspective on commodification, this research is presented in a qualitative descriptive way with the research locus in the Cirebon region, West Java. The purpose of this research is to find out the commodification practice of Tarling’s music in the digitalization field, including the production, distribution and consumption sub-fields. As a result, commodification is carried out by producer agents, managers, and singers, who tend to be submissive and adaptive to the production structure built through the role of agents in their respective habitus. The production, distribution, and consumption of Tarling music tends to ignore the value of authenticity that the practice of commodification in the digitalization fields produces a pseudo-culture due to standardization and uniformity of form, in which Tarling music becomes a profit commodity.&#x0D; Keywords: commodification, Tarling music, digitalization, Mosco, Bourdieu

The state of art and review on video streaming

Nowadays, video streaming is very popular around the world, users use video streaming to watch online movies, education, and do office work. Video streaming is referred to as the transmission of video content, live or recorded from server/cloud to end-users. Video and music files are prearranged and transmitted in sequential packets of data so they can be streamed instantaneously, only the User required a high-speed network for access, and a subscription to streaming via an application. In this paper, we survey and analyze the previous development in video streaming such as 2D, and 3D video streaming, compression technologies, protocols for streaming, cloud video processing, 4K/8K, and challenges, and limitations and offer aspects of future development, which will help to provide quality of service of video streaming and increase the revenue for service providers.

Modification of Digital Content and Digital Services Within the Understanding of the Directive 2019/770 and Draft Law № 6576

Modification of Digital Content and Digital Services Within the Understanding of the Directive 2019/770 and Draft Law № 6576

Effect of Subliminal Auditory Stimulation on Components of Auditory Late Responses and Functional Magnetic Resonance Imaging Data in Adults with Normal Hearing

Background and Aim: The use of subliminal stimulation for unconscious effects on the target population is of great importance. while several studies have generated proper visual and auditory stimuli for subliminal stimulation, no study was found on the long-term effects of it. Therefore, this study aims to investigate the long-term effects of auditory subliminal presentation on the central nervous system structures using fMRI and Auditory Late Responses (ALRs). Methods: Participants were 26 students with a mean age of 24.03±2.32 years. There was four group in study. First, fMRI was done and ALRs were recorded for all of them. Then, music files containing words embedded in them was presented subliminally to participants in groups A and B for 10 days, group C received music file without any subliminal stimuli and group D was control group. It was repeated after 10 days. Results: The subliminal stimuli had significant effects on the amplitudes of P1, N1, P2, and P3 waves (F3=25.03, 25.41, 39.11, and 14.60; p&lt;0.001). Between-group comparison showed significant changes in groups A and B compared to groups C and D (p&lt;0.05). The difference in the recorded potential mean values showed the highest change for recording electrodes in the prefrontal, frontal, and central regions and the lowest change in parietal and occipital regions. There was no significant change for a latency component. Conclusion: Subliminal stimuli, presented appropriately and continuously, can leave long- term effects on the central nervous system structure causing extensive changes in the people’s attitude to a certain subject. Keywords: Subliminal perception; unconscious perception; unconsciousness; subconsciousness; subliminal auditory stimulus

Automatic Classification of Music Genres Using the Deep Learning Approach

Abstract: Music is divided into arbitrary groups known as genres. Music genre classification is a challenging task due to the subjective and ambiguous nature of musical genres. The existing systems for music genre classification suffer from low accuracy and poor generalization of new data. Therefore, there is a need to develop a robust and accurate machine-learning model that can overcome these challenges and classify music audio files into different genres with high accuracy. The main aim of the Music genre classification project is to develop a user-friendly application that accepts audio files as input and classifies the audio file into a particular category of sound to which they belong (to predict its genre) using machine learning models. This application automates the process to reduce manual error and time. It will take an audio file as input and categorizes each file into a particular category like audio belonging to Disco, hip-hop, etc. The final classification is obtained from the collection of individual data. This machine learning model makes use of Support Vector Machine(SVM) and Logistic Regression models. Both models will be integrated into a website to make the project easily accessible.

A STUDY OF MUSICAL STRUCTURE AND PANTUN TEXTS IN JONGGAN PERFORMING ARTS AMONG DAYAK KANAYATN

Jonggan is a popular performing art in the Kanayatn Dayak community in West Kalimantan. Jonggan performance art is a traditional art that the Dayak Kanayatn people have from generation own to generation. The Jonggan's performance is a blend of entertaining music, dance, and chanting rhymes. Along with the rapid development of technology, the function and role of musicians in the show have disappeared. The Jonggan art form that has been shown lately has changed characters, namely (1) dance movements, (2) costumes worn, (3) the presence of whistle-blowers, and (4) the presence of curtains. From the 1960s to 1970s, Jonggan art still existed. After that, from the 1980s until now, Jonggan's existence is almost unknown, especially among millennials, and even if it is shown, it has transformed. At this time, the term Jonggan was known as Jongset (Jonggan Kaset), meaning Jonggan, which was performed using cassette media (cassette = CD, DVD, or music files in Flashdisk); this eliminated the essence of traditional musical instruments. This reason raises the question, what is the structure of the accompaniment of rhymes in the form of melody and rhythm in the Jonggan performance art? The method in this research plan uses a descriptive method in a qualitative form with a musicological approach. The data can be seen directly from Jonggan's current existence. The research results on the structure of rhyme accompaniment music in the Jonggan performing arts are to maintain the preservation and understanding of material regarding traditional music so that it is better organized; Jonggan will become a performance industry based on culture and have an economic impact (creative economy). The musical structure in the Jonggan performing arts uses arguing, dau, gadobokng, and solving musical instruments.