Piano Information Teaching Mode Based on Deep Learning Algorithm

Wang, XieHong

doi:10.1155/2022/6205763

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Wang [23] have presented The curriculum objectives, education and training objectives, and classroom teaching objectives made up the three categories into which the teaching objectives were divided. It was decided to construct an interactive classroom, learning area, teaching platform, resource platform, and cloud application platform for piano knowledge.…”

Section: Literature Surveymentioning

confidence: 99%

Hierarchically Gated Recurrent Neural Network optimized with Tasmanian Devil Optimization for Piano Playing Based on Virtual Reality Technology

LingCao Wu

2024

jes

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Playing the piano is a beautiful and extremely technical art form in addition to an elegant performance art. The player's ability to execute with elegance and technicality which requires years of practice will be crucial. The success of a piano concert ultimately rests not only on the performing abilities of the pianist but also on the pianist's mental and psychological makeup. This manuscript presents a Hierarchically Gated Recurrent Neural Network (HGRNN) optimized with the Tasmanian Devil Optimization (TDO) for predicting the real time instruction for playing piano based on virtual reality (PPVR-HGRNN-TDO). Initially, the data is collected from MIDI dataset. Afterward, the data is fed to a Multi-domain collaborative Filter (MDCF) based preprocessing process. Then the preprocessed data’s are fed to Scaling-Basis Chirplet Transform (SBCT) for extracting features such as tone quality and loudness variations. The extracted features are fed to HGRNN to classify the piano playing performance such as good, normal and poor. The weight parameters of HGRNN are optimized using TDO. The proposed PPVR-HGRNN-TDO is implemented in python, effectiveness assessed by several performance metrics such as accuracy, precision, specificity, sensitivity, F1-score and ROC. The gained results of the proposed PPVR-HGRNN-TDO method attains higher accuracy 20.46%, 23.54%, 35.58%, higher precision 33.56%, 21.72%, 33.97% and higher sensitivity 32.54%, 22.76%, 36.97%. The proposed method shows better results in all existing systems like Dual Channel Convolutional Neural Network (DCCNN),Convolutional Neural Network (CNN), and Back Propagation Neural Network with Genetic Algorithm (BPNN-GA). From the result it is concludes that the proposed PPVR-HGRNN-TDO method based accuracy is higher than the existing methods.

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Section: Literature Surveymentioning

confidence: 99%

Hierarchically Gated Recurrent Neural Network optimized with Tasmanian Devil Optimization for Piano Playing Based on Virtual Reality Technology

LingCao Wu

2024

jes

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Design and Implementation of Remote Piano Teaching Based on Attention-Induced Multi-Head Convolutional Neural Network Optimized with Hunter–Prey Optimization

Song

2024

Int J Comput Intell Syst

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The continuous progress of multimedia technology in music educational institutions has led to the recognition of its importance in our country and society. The traditional approach to piano teaching has its limitations, which can be overcome by adopting alternative approaches to the instrument, using advances in science and technology. For pianist, expressing emotions and thoughts through music is crucial, and teachers can now use multimedia tools to exemplify their musical skills to students effectively. This manuscript proposes the Remote Piano Teaching Based on Attention-Induced Multi-Head Convolutional Neural Network Optimized with Hunter–Prey Optimization to improve the piano-teaching quality. At first, input data is taken from Piano Triad Wavset dataset. Afterward, the data are fed to preprocessing stage. The preprocessing stage involve data cleaning or scrubbing that is the process of identifying errors, inconsistencies, and incorrectness in a dataset with the help of adaptive distorted Gaussian matched filter. Then, the preprocessed output is fed to Attention-Induced Multi-Head Convolutional Neural Network (AIMCNN) for effectively predict the piano-teaching quality. The hunter–prey optimization (HPO) algorithm is proposed to optimize the parameters of Attention-Induced Multi-Head Convolutional Neural Network. The performance of the proposed technique is evaluated under performance metrics like accuracy, computational time, learning skill analysis, learning activity analysis, learning behavior analysis; student performance ratio and teaching evaluation analysis are evaluated. The proposed RPT-AIMCNN-HPO attains better prediction accuracy 12.566%, 12.075% and 15.993%, higher learning skill 15.86%, 15.26% and 16.25% compared with existing methods.

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Piano Information Teaching Mode Based on Deep Learning Algorithm

Cited by 2 publications

References 34 publications

Hierarchically Gated Recurrent Neural Network optimized with Tasmanian Devil Optimization for Piano Playing Based on Virtual Reality Technology

Hierarchically Gated Recurrent Neural Network optimized with Tasmanian Devil Optimization for Piano Playing Based on Virtual Reality Technology

Design and Implementation of Remote Piano Teaching Based on Attention-Induced Multi-Head Convolutional Neural Network Optimized with Hunter–Prey Optimization

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