RETRACTED: Light music online system based on FPGA and Convolutional Neural Network

Zhao, Liang

doi:10.1016/j.micpro.2020.103556

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Meanwhile, Colleges and Universities (CAUs) lack teachers and equipment to carry out music education. From the perspective of the entire music education sector, network technology greatly impacts market segmentation and the change in market patterns [ 1 – 3 ]. The music education model has improved alongside its industry standardization.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Xue

2022

Computational Intelligence and Neuroscience

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This work intends to classify and integrate music genres and emotions to improve the quality of music education. This work proposes a web image education resource retrieval method based on semantic network and interactive image filtering for a music education environment. It makes a judgment on these music source data and then uses these extracted feature sequences as the emotions expressed in the model of the combination of Long Short-Term Memory (LSTM) and Attention Mechanism (AM), thus judging the emotion category of music. The emotion recognition accuracy has increased after improving LSTM-AM into the BiGR-AM model. The greater the difference between emotion genres is, the easier it is to analyze the feature sequence containing emotion features, and the higher the recognition accuracy is. The classification accuracy of the excited, relieved, relaxed, and sad emotions can reach 76.5%, 71.3%, 80.8%, and 73.4%, respectively. The proposed interactive filtering method based on a Convolutional Recurrent Neural Network can effectively classify and integrate music resources to improve the quality of music education.

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Section: Introductionmentioning

confidence: 99%

“…From the perspective of the entire music education sector, network technology greatly impacts market segmentation and the change in market patterns [1][2][3]. e music education model has improved alongside its industry standardization.…”

Section: Introductionmentioning

confidence: 99%

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Xue

2022

Computational Intelligence and Neuroscience

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FPGA-Based Reconfigurable Convolutional Neural Network Accelerator Using Sparse and Convolutional Optimization

Gowda¹,

Madhavan²,

Rinaldi

et al. 2022

Electronics

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Nowadays, the data flow architecture is considered as a general solution for the acceleration of a deep neural network (DNN) because of its higher parallelism. However, the conventional DNN accelerator offers only a restricted flexibility for diverse network models. In order to overcome this, a reconfigurable convolutional neural network (RCNN) accelerator, i.e., one of the DNN, is required to be developed over the field-programmable gate array (FPGA) platform. In this paper, the sparse optimization of weight (SOW) and convolutional optimization (CO) are proposed to improve the performances of the RCNN accelerator. The combination of SOW and CO is used to optimize the feature map and weight sizes of the RCNN accelerator; therefore, the hardware resources consumed by this RCNN are minimized in FPGA. The performances of RCNN-SOW-CO are analyzed by means of feature map size, weight size, sparseness of the input feature map (IFM), weight parameter proportion, block random access memory (BRAM), digital signal processing (DSP) elements, look-up tables (LUTs), slices, delay, power, and accuracy. An existing architectures OIDSCNN, LP-CNN, and DPR-NN are used to justify efficiency of the RCNN-SOW-CO. The LUT of RCNN-SOW-CO with Alexnet designed in the Zynq-7020 is 5150, which is less than the OIDSCNN and DPR-NN.

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Unleashing the Potential of Every Child

Riapina

2024

Embracing Cutting-Edge Technology in Modern Educational Settings

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The chapter explores how artificial intelligence (AI) can transform the field of education by providing customized learning experiences that meet the strengths and requirements of students. By investigating a range of real-world case studies, this chapter analyzes how AI-driven, learner-centered technologies can unlock the capabilities of each child. This chapter also scrutinizes the impact of AI on student engagement, performance, and outcomes. Moreover, ethical considerations such as ensuring fair access and balancing technology with human interaction related to the assimilation of AI technologies in educational environments are discussed. The primary objective of this chapter is to provide educators, researchers, and policymakers with insights into thoughtfully using AI to promote learner-centric instruction that customizes curricula, feedback, and support for students. Ultimately, this study seeks to further discourse on the transformation of one-size-fits-all education into dynamic experiences that meet each child's unique capabilities.

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RETRACTED: Light music online system based on FPGA and Convolutional Neural Network

Cited by 3 publications

References 4 publications

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

FPGA-Based Reconfigurable Convolutional Neural Network Accelerator Using Sparse and Convolutional Optimization

Unleashing the Potential of Every Child

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