# Agile Teaching Framework for Music Education Software Development Curriculum Based on Deep Learning

Authors: Wenli Seng
Journal: Mobile Information Systems
 (2022)

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## Abstract

Music learning is changing as a result of high-quality instruction, progressing from shallow to deep learning over time. In-depth learning is a revolutionary teaching paradigm that focuses entirely on the perception and investigation of music by students, allowing them to completely experience the allure of music. It can assist students not only to learn more about music and improve their skills but also to grow their music literacy and improve their musical talent. In this study, a deep-learning-based agile teaching framework is proposed for music education software development courses. The framework is built on the Internet of Things (IoT), and each device is considered an IoT device. Data is recorded and transmitted via the IoT network using the wireless sensor network (WSN). With the use of a genetic algorithm, the WSN Randomized Search Method is employed to execute intelligent music data transfer. For online classes, various music datasets are used, and the performance is examined. The findings of this study demonstrate that the proposed method is effective in teaching music education to students.


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## Body

## 1. Introduction

Music education is changing very rapidly all over the world, as a result of rapid social and technological changes. Music education has numerous advantages that are extremely beneficial to students. Music has a favorable impact on a student’s academic performance, aids in the development of social skills, and gives a creative outlet, all of which are important for a student’s development. Music education propels a student’s learning to new heights, and as a result, it should be considered an integral part of a child’s educational process at all times [1].A significant and necessary addition to music education in the twenty-first century, group instruction using the Agile Development Instructional Framework (ADIF) is an important and necessary addition to the standard teacher-led choir rehearsal, despite the need for additional implementation and feedback to fully assess student productivity and understanding [2]. With the help of ADIF, music education can be held to higher standards of quality and responsibility while also creating a culture of active student-centered problem-solving, cooperation, and accountability among its students and faculty. Music educators have the ability and the responsibility to provide a world-class musical education to their students and colleagues, regardless of their background. If they want to be at the forefront of creative pedagogical practices, they must acknowledge and embrace the past, present, and future of music education [3]. Traditional educational methodologies and teaching preferences may no longer be acceptable in the field of music education, which is only now beginning to grapple with these new developments and concerns. Group instruction based on Agile Development concepts is intended to be a supplement to classroom instruction rather than a replacement for it. In contrast to the traditional director-led teaching model, Agile-Based Instruction is built on the foundation of collaborative learning [4]. It is the responsibility of the music instructor to guarantee that students obtain the specified curricula, as well as to construct and provide rehearsal content. A significant component of performance is the selection of repertory and keeping up to date with local and national conventions and conventions [5]. Student learning is encouraged to extend beyond the usual performance-based music environment through the use of ADIF. Students are made aware of their responsibility for the subject matter and rehearsal process, as well as their active participation in music education [6].Recent advances in music psychology literature have begun to underline the fact that music is a highly social activity. There is an increasing amount of literature that highlights the key impact which peer groups, the family, the relationships between teacher and pupil, and between pupils themselves have upon a child’s interest in and knowledge about kinds of music. MacDonald and Miell [7] studied two projects that investigated the aspects of the intrinsically social nature of musical participation. One project looked into the communication (both verbal and musical) that took place during the creation of works by friends and no friends. The project study focused on how people with exceptional disabilities improved their musical and communication skills after attending gamelan workshops. Görner et al. [8] concluded by extending the social-cultural viewpoint to current developments in music education by reviewing some studies on musical style sensitivity as an example of this broad approach. As a result, two new conceptual models were proposed, one for the opportunities offered by music education in the twenty-first century and the other for the possible outcomes of this musical education. This article introduces the Agile Development Instructional Framework (ADIF), a new methodology for music education based on principles of the Agile Development Philosophy. This new educational framework introduces a rehearsal paradigm that values integrative teacher/student collaboration, student-initiated and directed small group sectionals, and the choir members’ interactions and individual musical growth. Hargreaves et al. [9] introduced the Agile Development Instructional Framework (ADIF) as a new paradigm for music teaching based on the Agile Development Philosophy’s principles. This unique pedagogical framework presents a rehearsal paradigm that prioritizes integrative teacher/student collaboration, student-directed small group sectionals, and choir members’ connections and musical growth. The author in [10] used the agile software development process to aid in the development of a successful musical game design on an Android device with a multitouch screen. During testing, the data were collected based on empirical observations and gaming recordings on tablets to identify difficulties and possible solutions in the design of e-learning musical games. Chung and Wu [11] presented a music composition game that combines rhythmic, melodic, and chord progression improvisation exercises, as well as an interactive platform for specialized hand position and technique instruction. Chung and Chen [12] proposed an innovative conceptual model for musical education. The critical elements considered in this model included music teaching methods, learning styles, the online technology environment, and student abilities and knowledge. It was proposed that together, these parts form the foundation for a conceptual model that serves as a way of supposing rather than codifying a musical process.In this study, the current trend of online music classes is investigated. To be more specific, traditional Chinese music is being studied. For the online music class, an artificial intelligence (AI) model is created using the Internet of Things (IoT) with the help of WSN. An AI-based reinforcement learning algorithm is implemented to automate the process of automatic teaching and learning. In this online music class, the genetic algorithm is combined with WSN to implement the Randomized Search Method (RSM). An RSM is utilized to check the music’s frequency level and aid in the automatic transfer to another wavelength within the dataset. Instead of this scenario, offline classes are converted to online courses with an online agile teaching framework for music education. As a result of this conversion, the agile teaching framework for music education has become slightly more sophisticated by incorporating various computer-based technologies. The results of this study show that the proposed strategy is beneficial in teaching students music education.The rest of the manuscript is organized as follows: Section2 provides a detailed description of the proposed method and the agile teaching method. The results of the proposed method are illustrated in Section 3, and Section 4 concludes the manuscript.


## 2. Materials and Methods

### 2.1. Agile Teaching/Learning Methodology

The agile teaching/learning methodology (ATLM) is a higher-education teaching/learning methodology based on best practices and ideas from the field of software engineering, as well as concepts from agile software approaches. ATLM places a focus on adaptability, communication, and the learning process [13–16]. The proposed agile teaching and learning framework is represented in Figure 1. This agile framework is implemented to improve performance for teaching and learning music courses online. This process works with the support of intelligent wireless systems along with sensor networks. The agile framework does the role of repeating the process of teaching and learning until the expected performance is achieved. In this framework, other technologies are playing a vital role, and some of them are described in the following points.Figure 1
Agile teaching and learning framework.In this ATLM framework, the teaching process will be started with basic methodology, scope, and goals of teaching and guidelines for successful teaching. These methodologies are followed for the course, and this will enable the students to learn by incorporating certain methods. The process is completed with the evaluation of the student’s learning mechanism and feedback from the student. In this last phase, certain upgradations are made, and the complete process is repeated. In this study, the concept of the Scrum agile framework is implemented to deliver the expected results. An agile framework-based teaching mechanism works with the support of certain interactive devices. To make the teaching and learning process interactive, the IoT is utilized for effective interaction. The IoT is a network of smart devices that allows for easy communication between nodes via wireless networks with the help of the Internet. This IoT performs the function of sharing the network with the devices to transmit and process data. In the proposed application, each user (student and teacher) is considered an IoT user. These IoT users interact with each other as they are sharing the wireless network and are connected through IoT devices (mobile phones, laptops, and others). Sharing of resources involves the course materials prepared by the teachers. In this music teaching and learning, the musical instruments are connected with certain sensors to provide a higher quality of music recording. This process will help the students improve their learning process in the music course. The course materials can include prerecorded videos or audio that can be played during the scheduled session. A deep learning mechanism is incorporated to train the system for autoresponses to the student’s feedback and classification of music based on the frequency distribution of the music and also to provide choices to the student in selecting the music for better understanding.


### 2.2. Proposed Work

A genetic algorithm is a heuristic technique used in machine learning to solve optimization problems [17]. This algorithm can solve a difficult task that would otherwise take an inordinate amount of time. It has been used in various real-life applications such as data centers, electronic circuit design, code-breaking, image processing, and artificial creativity [18–20]. In this study, the genetic algorithm is used with signals of the WSN Randomized Search Method; the time delay of this signal is computed as given in the following equation:
(1)∫=ϑ∑k=1hhcosθk,where θ is the direction of a long-distance signal, h denotes the distance between array members, k is the maximum speed, and ϑ is the time delay of the array object. As an outcome, the time delay among both array items can also be determined using the following equation:
(2)ϑ=∫ehωτ=∑ehωpcosθ/k=∑eh2πhcosθ/ρ∫o,where∫o represents the frequency in the middle. Equation (3) represents the phase angle for short-range signals.
(3)ϑ=∑h=1ρeh2πhcosθ/ρ,where ρ is the total wavelength of the signal; as a result, if the signal’s time delay is known, the signal’s direction can be calculated using equation (3), which is the fundamental of efficient spectrum estimation approaches. Equation (4) represents how the eigenvectors of the matrix Dx are arranged according to their size.
(4)ρ1≥ρ2≥⋯≥ρx>0,where matrix Dx has eigenvectors that correspond not only to signal but also to noise, respectively. If ρi is the ith amplitude of the matrix Dx and vi is the associated eigenvector ρi, then the following equation is obtained:
(5)DxKi=ρiKi.Letρi=μ2 represent the minimum of Dx which is determined by equations (6) and (7).
(6)DxKi=σ2μ2Kii=q+1,q+2,..⋯,x,(7)μ2Ki=∑i=1sDsDK+μ2IDiWhenever the right side is extended and compared to the left, the result is as given in the following equation:
(8)∑QDsKKGi=0,where Q denotes the signal edge value, G is the end of an edge value in equation (8), and g denotes the start of another signal. S represents the maximum speed. The frequency level is denoted by the letter i. Q raises the frequency level. D specifies the matrix’s eigenvectors. Ds exists because AHA is a D∗D full-level matrix and (AHA)-1 exists. Using this, we calculate Ds (AHA)-1A H on both edges at the same time, and equation (9) is obtained.
(9)∑Ds−1QkK−1QkKDsQkDi=0,where QkDi=0i=q+1,q+2,⋯⋯..,x.Next, we create a noise matrixKn with the noise distribution as each column in the following equation:
(10)Kn=Dq+1,Dq+2,⋯,Hx.Kxuθ can be calculated using equation (11) and equation (12), respectively, to describe its spectrum.
(11)Kxuθ=∑n=1x1Dx0KnKnxgθ=∑x=1n1Knxg02.The relevance corresponding to an intermediate value in piano music is denoted byg0, and the paranoid concept is denoted by1/Dx0KnKnxgθ. The correspondence can be represented in equation (12) which is a representation and computation for the provinceKxuθ.
(12)Fxuθ=∫n=1x=11Dx0KnKnxgθIt is comparable to the intensity of piano music audio. It is indeed a measure of a music signal’s totality as well as its spatial frequency elements are calculated by utilizing the following equation:(13)Signal=∫n=1x∑n=1xgDK∑n=1xDK,where Dk appears to be the amplitude. Equation (14) is used to compute the sequence-s spectral bandwidth:
(14)BW=∫∑nDkfk−fns1/s,where Dk denotes the spatial magnitude.It is the classical music frequency below in which a significant portion of spatial structure energy, 98%, can be found. It is computed as follows:(15)Dk=12∑xsigko+∑n=1ksigko−1xn−o,where gt is the eigenvector and st−1 is the candidate state corresponding to such a piano music current time, but also gratherk are also the musical matrices. The notification with the most recent nationalst, which relates to the network’s intended goal, can be shown in the following equation:
(16)sigkg=∫1Dg≥0,−1Dg<0,where g∫t represents the bearing, g∫t is the paranoid time, kt represents the input variable only at a specific frequency, and st represents the outside situation at the original period. The piano music transformer information can also be described as given in equation (17). The matrix corresponding to the input entrance is denoted by g, and the deranged period corresponding entrance is denoted byki.
(17)∫ts=∑k=1tμg∫t.s.gt+kt.


## 2.1. Agile Teaching/Learning Methodology

The agile teaching/learning methodology (ATLM) is a higher-education teaching/learning methodology based on best practices and ideas from the field of software engineering, as well as concepts from agile software approaches. ATLM places a focus on adaptability, communication, and the learning process [13–16]. The proposed agile teaching and learning framework is represented in Figure 1. This agile framework is implemented to improve performance for teaching and learning music courses online. This process works with the support of intelligent wireless systems along with sensor networks. The agile framework does the role of repeating the process of teaching and learning until the expected performance is achieved. In this framework, other technologies are playing a vital role, and some of them are described in the following points.Figure 1
Agile teaching and learning framework.In this ATLM framework, the teaching process will be started with basic methodology, scope, and goals of teaching and guidelines for successful teaching. These methodologies are followed for the course, and this will enable the students to learn by incorporating certain methods. The process is completed with the evaluation of the student’s learning mechanism and feedback from the student. In this last phase, certain upgradations are made, and the complete process is repeated. In this study, the concept of the Scrum agile framework is implemented to deliver the expected results. An agile framework-based teaching mechanism works with the support of certain interactive devices. To make the teaching and learning process interactive, the IoT is utilized for effective interaction. The IoT is a network of smart devices that allows for easy communication between nodes via wireless networks with the help of the Internet. This IoT performs the function of sharing the network with the devices to transmit and process data. In the proposed application, each user (student and teacher) is considered an IoT user. These IoT users interact with each other as they are sharing the wireless network and are connected through IoT devices (mobile phones, laptops, and others). Sharing of resources involves the course materials prepared by the teachers. In this music teaching and learning, the musical instruments are connected with certain sensors to provide a higher quality of music recording. This process will help the students improve their learning process in the music course. The course materials can include prerecorded videos or audio that can be played during the scheduled session. A deep learning mechanism is incorporated to train the system for autoresponses to the student’s feedback and classification of music based on the frequency distribution of the music and also to provide choices to the student in selecting the music for better understanding.


## 2.2. Proposed Work

A genetic algorithm is a heuristic technique used in machine learning to solve optimization problems [17]. This algorithm can solve a difficult task that would otherwise take an inordinate amount of time. It has been used in various real-life applications such as data centers, electronic circuit design, code-breaking, image processing, and artificial creativity [18–20]. In this study, the genetic algorithm is used with signals of the WSN Randomized Search Method; the time delay of this signal is computed as given in the following equation:
(1)∫=ϑ∑k=1hhcosθk,where θ is the direction of a long-distance signal, h denotes the distance between array members, k is the maximum speed, and ϑ is the time delay of the array object. As an outcome, the time delay among both array items can also be determined using the following equation:
(2)ϑ=∫ehωτ=∑ehωpcosθ/k=∑eh2πhcosθ/ρ∫o,where∫o represents the frequency in the middle. Equation (3) represents the phase angle for short-range signals.
(3)ϑ=∑h=1ρeh2πhcosθ/ρ,where ρ is the total wavelength of the signal; as a result, if the signal’s time delay is known, the signal’s direction can be calculated using equation (3), which is the fundamental of efficient spectrum estimation approaches. Equation (4) represents how the eigenvectors of the matrix Dx are arranged according to their size.
(4)ρ1≥ρ2≥⋯≥ρx>0,where matrix Dx has eigenvectors that correspond not only to signal but also to noise, respectively. If ρi is the ith amplitude of the matrix Dx and vi is the associated eigenvector ρi, then the following equation is obtained:
(5)DxKi=ρiKi.Letρi=μ2 represent the minimum of Dx which is determined by equations (6) and (7).
(6)DxKi=σ2μ2Kii=q+1,q+2,..⋯,x,(7)μ2Ki=∑i=1sDsDK+μ2IDiWhenever the right side is extended and compared to the left, the result is as given in the following equation:
(8)∑QDsKKGi=0,where Q denotes the signal edge value, G is the end of an edge value in equation (8), and g denotes the start of another signal. S represents the maximum speed. The frequency level is denoted by the letter i. Q raises the frequency level. D specifies the matrix’s eigenvectors. Ds exists because AHA is a D∗D full-level matrix and (AHA)-1 exists. Using this, we calculate Ds (AHA)-1A H on both edges at the same time, and equation (9) is obtained.
(9)∑Ds−1QkK−1QkKDsQkDi=0,where QkDi=0i=q+1,q+2,⋯⋯..,x.Next, we create a noise matrixKn with the noise distribution as each column in the following equation:
(10)Kn=Dq+1,Dq+2,⋯,Hx.Kxuθ can be calculated using equation (11) and equation (12), respectively, to describe its spectrum.
(11)Kxuθ=∑n=1x1Dx0KnKnxgθ=∑x=1n1Knxg02.The relevance corresponding to an intermediate value in piano music is denoted byg0, and the paranoid concept is denoted by1/Dx0KnKnxgθ. The correspondence can be represented in equation (12) which is a representation and computation for the provinceKxuθ.
(12)Fxuθ=∫n=1x=11Dx0KnKnxgθIt is comparable to the intensity of piano music audio. It is indeed a measure of a music signal’s totality as well as its spatial frequency elements are calculated by utilizing the following equation:(13)Signal=∫n=1x∑n=1xgDK∑n=1xDK,where Dk appears to be the amplitude. Equation (14) is used to compute the sequence-s spectral bandwidth:
(14)BW=∫∑nDkfk−fns1/s,where Dk denotes the spatial magnitude.It is the classical music frequency below in which a significant portion of spatial structure energy, 98%, can be found. It is computed as follows:(15)Dk=12∑xsigko+∑n=1ksigko−1xn−o,where gt is the eigenvector and st−1 is the candidate state corresponding to such a piano music current time, but also gratherk are also the musical matrices. The notification with the most recent nationalst, which relates to the network’s intended goal, can be shown in the following equation:
(16)sigkg=∫1Dg≥0,−1Dg<0,where g∫t represents the bearing, g∫t is the paranoid time, kt represents the input variable only at a specific frequency, and st represents the outside situation at the original period. The piano music transformer information can also be described as given in equation (17). The matrix corresponding to the input entrance is denoted by g, and the deranged period corresponding entrance is denoted byki.
(17)∫ts=∑k=1tμg∫t.s.gt+kt.


## 3. Results and Discussion

Recent advancements in wireless technology have resulted in several self-contained deployments of IoT networks. Because nodes in multiple systems coexist, all broadcasters and receivers must be aware of their surroundings’ audio signal to adjust the settings of transmitters and receivers to meet their demands. Recently, learning approaches have grown in popularity due to their ability to learn, analyze, and estimate transmitter signals and the associated factors that characterize their frequency in the IoT domain. The genetic algorithm with the WSN Randomized Search Method for constructing a reliable framework to track pirate frequency transmitters employs an AI with IoT audio signal data that is received as input signals. Once the hostile transmitters have been detected and eliminated, this genetic algorithm with WSN Randomized Search Method feature coding is used to categorize the authorized transmitters. It is based on the agile teaching framework for music education/transmission and incorporates strategies developed by the genetic algorithm with the WSN Randomized Search Method. When we examine these genetic algorithms with the WSN Randomized Search Method, we can see that they help distinguish between the agile teaching framework for music education and IoT learning in a spectrum. The wave signal shown in Figure2 will be included in the music dataset.Figure 2
Frequency wave graph for the music data 1.When a classical music wave signal is fed into the system, the expected output is level frequency, time duration, and power consumption. The music dataset used in this study contains 13 musical datasets representing traditional Chinese music. Among them is the traditional Chinese music named Chun Jiang Hua Yue Yie.mid, used in this proposed method. Students can learn about the names and tunes of music by using these datasets (refer to Table1) from the IoT network website. This frequency analysis for the online music class is based on the teaching and learning categories. Based on these values, Figure 2 shows the normal frequency mu=0.87999 and sigma=0.08974. The music will be played when the value is 1, and the frequency result analysis for the online classical music education will be obtained, to analyze the time (s) (0.762165 s) to play the music, based on the power (dB) (0.0011) and the overall accuracy (97.67%) in the online music class (refer to Table 1).Table 1
Chun_Jiang_Hua_Yue_Yie.mid dataset with music 1 outcome of frequency analysis for online classical music education.
Frequency datasetGenetic algorithm with WSN Randomized Search MethodChinese traditional musicChun Jiang Hua Yue Yie.midTime (s)Power (dB)FrequencyOverall accuracy (%)Music 10.7621650.00110.998997.67The time-frequency parameters of an audio signal are made up of medium-level signal characteristics as shown in Figure3. They are not qualitatively motivated and categorize a signal’s distinctness in the IoT time-space domain or frequency.Figure 3
Chun Jiang Hua Yue Yie.mid dataset using music 2 frequency analysis for online classical music education.Because music has a wide range of temporal variations, physical feature extraction is performed in short intersecting windows as shown in Table2. In the music to be played, the frequency based on the proposed method evaluates the time (s) to play and the power (dB). To learn the music audio/video, the students use medium-level signal frequency analysis to categorize the music frequency. The music quality motivates and categorizes the signals in time (0.724866 s) to play and specify the frequency (0.9746f) based on the IoT because the music has such a range and overall accuracy (98.45%) of temporal variations.Table 2
Chun Jiang Hua Yue Yie.mid dataset using music 2 frequency analysis for online classical music education.
Frequency datasetGenetic algorithm with WSN Randomized Search MethodChinese traditional musicChun Jiang Hua Yue Yie.midTime (s)Power (dB)FrequencyOverall accuracy (%)Music 20.7248660.00130.974698.45In this circumstance, offline classes are transformed into online courses, also known as virtual classes, with virtual classrooms and the results are shown in Figure4. As a result of this conversion, the agile teaching framework for music education has become slightly more advanced by incorporating various computer-based technologies. AI is one of them. The time and frequency specifications of an audio signal are high-level amplitude and phase. They are not qualitatively encouraged and classify a signal’s distinctness in the space-time or frequency field.Figure 4
Chun Jiang Hua Yue Yie.mid dataset using music 3 frequency analysis for online classical music education.Because music already has a wide range of spatial and temporal variabilities, the physical features are extracted in short, interrelated window frames as given in Table3. The analysis for the online classes for learning and teaching music functions is based on the AI with IoT network. The time-based learning of audio/video music by students in offline classes has been replaced by online virtual courses. The adaptive teaching framework for playing the music based on the node is specified in the IoT network with AI. To evaluate the frequency which is 99.94%, we used the time (0.612165 s) and power (0.0011 dB), respectively. The time specifies the number of times to play for the particular student to analyze the frequency range. Because they are not taking into account the music quality, the purpose of the music is only to set the field for playing the music and learning the student (Table 3).Table 3
Chun Jiang Hua Yue Yie.mid dataset using music 3 frequency analysis for online classical music education.
Signal datasetGenetic algorithm with WSN Randomized Search MethodTraditional Chinese musicChun Jiang Hua Yue Yie.midTime (s)Power (dB)FrequencyOverall accuracy (%)Music 30.6121650.00110.942799.94In this situation, connected to the Internet, classes have been transformed to distance learning, also known as virtual classes, with an online agile teaching framework for music education using the IoT framework for music education. An audio signal’s time-frequency parameters comprise various music transceiver signal features. They are still not qualitatively motivated and describe the distinctness of a signal in the space-time or frequency scope. Because music contains a vast spectrum of variability, physical feature extraction is accomplished in short, using overlapping window panes (Table4). Figure 5 depicts the study of a student taking online music classes, which combines IoT and AI technology to link students to online courses and compare music.Table 4
Chun Jiang Hua Yue Yie.mid dataset using music 4 frequency analysis for online classical music education.
Signal datasetGenetic algorithm with WSN Randomized Search MethodChinese traditional musicChun Jiang Hua Yue Yie.midTime (s)Power (dB)FrequencyOverall accuracy (%)Music 40.7367380.00140.964292.61Figure 5
Chun Jiang Hua Yue Yie.mid dataset using music 4 frequency analysis for online classical music education.The playing video and audio frequency are the same or different. It analyzes the spectrumE_mu based on the frequency range to play and learn the audio and video in a particular iteration, because it considers the music playback quality to set the limit for playing the music. Finally, as the iteration progresses, the system will automatically play another song while reporting the distance between the signal and time (0.736738 s) with frequency (0.9642f) based on overall accuracy (92.61%).In Figure6, the different spectral specifications of an audio signal are composed of various music transmit and receive signal features. They are not perceptually motivated and characterize a signal’s distinctness in the sequential or frequency field. Short, intersecting windows are used to extract physical features. The current trend of online music classes is considered because music has such a wide range of temporal variations.Figure 6
Chun Jiang Hua Yue Yie.mid dataset using music 5 frequency analysis for online classical music education.To be more specific, for the online music course, an AI with IoT model was created with the help of WSN. The results are given in Table5. The student must conduct an analysis based on their motivation to attend online-offline classes and characterize a signal’s distance in time with frequency range. The student feedback based on them limits the playing of the music in online classes and sets the iteration to play the music. Once the end of the iteration automatically transfers another node, the node is specified for the AI with IoT. The student feedback is based on the music played and also fixed for the iteration to play because the music quality and current trending focus of the online music classes are considered.Table 5
Chun Jiang Hua Yue Yie.mid dataset using music 5 frequency analysis for online classical music education.
Signal datasetGenetic algorithm with WSN Randomized Search MethodTraditional Chinese musicChun Jiang Hua Yue Yie.midTime (s)Power (dB)FrequencyOverall accuracy (%)Music 50.6802710.00150.949898.73An audio signal’s spectral specifications are composed of various music components, and the overall accuracy (98.67%) for the analysis of the features of the transmitter and receiver is given in Table6. To extract physical features, short intersecting windows are used. Even though music has a massive spectrum of temporal variations, the most popular online music class is considered. Traditional Chinese music, in particular, is being studied. With the help of WSN, an AI with IoT model has been developed for musical online music classes.Table 6
Overall accuracy.
Frequency datasetGenetic algorithm with WSN Randomized Search MethodChinese traditional musicChun Jiang Hua Yue Yie.midTime (s)Power (dB)FrequencyOverall accuracy (%)Music 10.7621650.00110.998997.67Music 20.7248660.00130.974698.45Music 30.6121650.00110.942799.94Music 40.7367380.00140.964292.61Music 50.5702710.00140.989197.81Music 60.680270.00150.949898.73Accuracy98.67


## 4. Conclusion

Nowadays, the agile teaching framework is used in the educational field. With the help of this framework, music education has given students a chance to learn. The introduction of this agile framework into the proposed system is aimed at improving online music course teaching and learning performance. It works with the help of sophisticated wireless systems and sensor networks. It can help students not only to learn more about music and enhance their skills but also to develop their musical literacy and talent. In this study, an agile teaching framework for music education software development is proposed. The framework employed IoT and associated devices. The WSN is used to record and transmit data via the IoT network WSN. The WSN Randomized Search Method is used to accomplish intelligent music data transmission using a genetic algorithm. Various music datasets are evaluated for online classes, and the performance is compared. The study results proved that the proposed system works well in analyzing the performance of music education and also helps in teaching music.



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*Source: 5243849-2022-05-16.xml*