{"title":"利用 Moodle 平台设计支持深度学习的大学音乐教学系统","authors":"X F Chen","doi":"10.5750/ijme.v1i1.1349","DOIUrl":null,"url":null,"abstract":"Music education plays a vital role in fostering creativity, expression, and cognitive development among students in university settings. Moodle is the learning management platform to promotes significant knowledge sharing among the students in the Universities. In this paper, introduce the Federated Deep Learning Moodle Hidden Chain (FDLMHc) with the Moodle platform for music education experiences. The FDLMHc system combines the power of federated learning with the flexibility of Moodle to provide personalized feedback and adaptive learning pathways for students. The FDLMHc model uses the Music signal pitch estimation with the consideration of the different pitches in the signal frequencies. The signal of the music signal is estimated for the different SNR rates of -10dB, 0dB, 10 dB, and 20 dB. The proposed FDLMHc model computes and processes the music signal with the hidden chain process for the estimation of the pitches in the music signal. The estimated hidden chain model is applied over the federated learning network for the classification of the signal in the Music. The findings reveal promising results, demonstrating the system's ability to accurately classify musical elements, such as pitch, rhythm, and dynamics, while providing personalized feedback tailored to individual student needs. The accuracy for the estimation of the Music pitch is estimated as the 95% with a convergence rate of 91% for the estimation of the signal in the Music signal.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Designing a Deep Learning-Enabled Music Teaching System in Universities Using the Moodle Platform\",\"authors\":\"X F Chen\",\"doi\":\"10.5750/ijme.v1i1.1349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Music education plays a vital role in fostering creativity, expression, and cognitive development among students in university settings. Moodle is the learning management platform to promotes significant knowledge sharing among the students in the Universities. In this paper, introduce the Federated Deep Learning Moodle Hidden Chain (FDLMHc) with the Moodle platform for music education experiences. The FDLMHc system combines the power of federated learning with the flexibility of Moodle to provide personalized feedback and adaptive learning pathways for students. The FDLMHc model uses the Music signal pitch estimation with the consideration of the different pitches in the signal frequencies. The signal of the music signal is estimated for the different SNR rates of -10dB, 0dB, 10 dB, and 20 dB. The proposed FDLMHc model computes and processes the music signal with the hidden chain process for the estimation of the pitches in the music signal. The estimated hidden chain model is applied over the federated learning network for the classification of the signal in the Music. The findings reveal promising results, demonstrating the system's ability to accurately classify musical elements, such as pitch, rhythm, and dynamics, while providing personalized feedback tailored to individual student needs. The accuracy for the estimation of the Music pitch is estimated as the 95% with a convergence rate of 91% for the estimation of the signal in the Music signal.\",\"PeriodicalId\":50313,\"journal\":{\"name\":\"International Journal of Maritime Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Maritime Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.5750/ijme.v1i1.1349\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Maritime Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5750/ijme.v1i1.1349","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
引用次数: 0
摘要
在大学环境中,音乐教育在培养学生的创造力、表现力和认知发展方面发挥着至关重要的作用。Moodle 是一个学习管理平台,可促进大学学生之间的知识共享。本文将结合 Moodle 平台,介绍用于音乐教育体验的 Federated Deep Learning Moodle Hidden Chain(FDLMHc)。FDLMHc 系统将联合学习的强大功能与 Moodle 的灵活性相结合,为学生提供个性化反馈和自适应学习途径。FDLMHc 模型使用音乐信号音高估计,并考虑到信号频率中的不同音高。在-10dB、0dB、10 dB 和 20 dB 的不同信噪比率下对音乐信号进行估计。所提出的 FDLMHc 模型利用隐链过程计算和处理音乐信号,以估算音乐信号中的音高。估计出的隐链模型被应用于联合学习网络,用于音乐信号的分类。研究结果表明,该系统能够准确地对音高、节奏和动态等音乐元素进行分类,同时提供符合学生个人需求的个性化反馈。估计音乐音高的准确率为 95%,音乐信号中信号估计的收敛率为 91%。
Designing a Deep Learning-Enabled Music Teaching System in Universities Using the Moodle Platform
Music education plays a vital role in fostering creativity, expression, and cognitive development among students in university settings. Moodle is the learning management platform to promotes significant knowledge sharing among the students in the Universities. In this paper, introduce the Federated Deep Learning Moodle Hidden Chain (FDLMHc) with the Moodle platform for music education experiences. The FDLMHc system combines the power of federated learning with the flexibility of Moodle to provide personalized feedback and adaptive learning pathways for students. The FDLMHc model uses the Music signal pitch estimation with the consideration of the different pitches in the signal frequencies. The signal of the music signal is estimated for the different SNR rates of -10dB, 0dB, 10 dB, and 20 dB. The proposed FDLMHc model computes and processes the music signal with the hidden chain process for the estimation of the pitches in the music signal. The estimated hidden chain model is applied over the federated learning network for the classification of the signal in the Music. The findings reveal promising results, demonstrating the system's ability to accurately classify musical elements, such as pitch, rhythm, and dynamics, while providing personalized feedback tailored to individual student needs. The accuracy for the estimation of the Music pitch is estimated as the 95% with a convergence rate of 91% for the estimation of the signal in the Music signal.
期刊介绍:
The International Journal of Maritime Engineering (IJME) provides a forum for the reporting and discussion on technical and scientific issues associated with the design and construction of commercial marine vessels . Contributions in the form of papers and notes, together with discussion on published papers are welcomed.