[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Kai, Hong

doi:10.1155/2021/9562579

Cited by 9 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the continuous improvement of human social development, music has become an essential part of people's daily life. People have been exploring and researching music for a long time, and the earliest record of music theory in China can be traced back to the pre-Qin period [1]; after which Zhu Zaiyu, a famous musicologist in the Ming Dynasty, proposed twelve average laws, which divided octaves into twelve equal parts and used them to improve musical instruments, greatly enhancing their expressive power [2]. After entering the 21st century, the rapid development of big data technology and computers has triggered a global wave of artificial intelligence learning.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Error Correction Techniques for Piano Playing Music Based on Convolutional Cyclic Hashing Method

Wang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Music as a sound symbol can express what people think; music is both a form of social behavior and can promote people’s emotional communication; music is also a form of entertainment and can enrich people’s spiritual life. In this paper, we propose a new convolutional recurrent hashing method CRNNH, which uses multilayer RNN to learn to discriminate piano playing music using convolutional feature map sequences. Firstly, a convolutional feature map sequence learning preserving similarity hash function is designed consisting of multilayer convolutional feature maps extracted from multiple convolutional layers of a pretrained CNN; secondly, a new deep learning framework is proposed to generate hash codes using a multilayer RNN, which directly uses the convolutional feature maps as input to preserve the spatial structure of the feature maps; finally, a new loss function is proposed to preserve the semantic similarity and balance of the hash codes, while considering the quantization error generated when the hash layer outputs binary hash codes. The experimental results illustrate that the proposed CRNNH can obtain better performance compared to other hashing methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Recognition and Error Correction Techniques for Piano Playing Music Based on Convolutional Cyclic Hashing Method

Wang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…He believes that classical music is regarded as an essential music culture in music education all over the world. Kai [21] designed a music feature recognition system utilizing IoT technology. e sensors of the IoT system will be placed in different positions to obtain music information to further complete the analysis and processing of music signals.…”

Section: Related Workmentioning

confidence: 99%

Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation

Zhang

Gong

2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Music art is a form of conveying cultural information and humanistic emotions. From ancient times to the present, the form of music has undergone great changes. The change of music form is closely related to the change in dynasties, the development of the market economy, and the change in humanistic spirit. Today, the development of music has reached a relatively prosperous stage, which is closely related to the rapid development of the market economy. At the heart of the quantitative analysis is the study of associations between data. However, it will be a difficult task to analyze the relationship between the development scale of the market economy and the form of music innovation only by artificial means. This research mainly uses a convolutional neural network and long and short-term memory neural network technology to quantitatively analyze the relationship between the development scale of the market economy and the form of music innovation, which is mainly a quantitative analysis of classical music, pop music, and rap music. The research results show that convolutional neural networks and long short-term memory neural networks have sufficient capabilities to quantitatively analyze the relationship between the market development scale and the form of music innovation. Neural networks with LSTM layers have lower errors in predicting market economic correlates than neural network methods without LSTM layers. The error is reduced by 0.27%. For the prediction of innovative forms of music, the largest prediction error is only 2.93%, which is closely related to the variability of popular music. The linear correlation indices for the predictions of the three forms of musical innovation also all exceed 0.955.

show abstract

“…Literature [13] envisions an intelligent guitar model that enables song retrieval from online music databases, and experts and players highly recognize the proposed approach, believing that the form of musical instruments directly connecting to the database satisfies the needs of music creation and instrument playing. Literature [14] aims to solve the problem of music feature recognition, develops a music feature recognition model with IoT technology as the underlying architecture, and conducts an example analysis. The experimental results show that the proposed music feature recognition model, which can accurately differentiate between the style and emotional characteristics of music, has great potential for application in music creation and music research.…”

Section: Introductionmentioning

confidence: 99%

Construction of a digital library of vocal music resources based on second-order data decomposition algorithm

Wang,

Guo,

Pan

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In this paper, in order to solve the modal aliasing problem of the EMD decomposition algorithm and EEMD decomposition algorithm and optimize the vocal audio data decomposition on the basis of the adaptive VMD algorithm, the EEMD-VMD second-order data decomposition algorithm is proposed to decompose the digitized time series of vocal music resources, and to update the construction of the digital library of vocal music resources. Combined with the decomposition steps of the second-order data decomposition algorithm in the vocal resources digitized library, the overall framework of the vocal resources digitized library is designed and constructed with the main line of vocal resource types and the secondary line of content categories. Design the experimental environment, preprocess the vocal audio signal features, explore the modal number K value size of the EEMD-VMD second-order data decomposition algorithm, analyze the vocal audio denoising performance of the algorithm, and compare the correlation coefficients of the IMF components of the decomposition algorithms with the original audio signals. Explore the second-order data decomposition algorithm’s emotional classification of vocal resources and the performance of vocal signal enhancement, add noise sources, and use subjective and objective evaluation methods to assess the quality of vocal PESQ and STOI after decomposition processing. The EEMD-VMD second-order data decomposition algorithm performs the most prominently in street noise environments. The enhancement results in the case of signal-to-noise ratios of -5, -10, and -15 are 3.59, respectively, 4.36, 4.29, and the mean value of the enhancement performance reaches 4.08. The second-order data decomposition algorithm’s precise processing of vocal resources provides better quality digital resources for the construction of a digital library of vocal resources.

show abstract

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Cited by 9 publications

References 27 publications

Recognition and Error Correction Techniques for Piano Playing Music Based on Convolutional Cyclic Hashing Method

Recognition and Error Correction Techniques for Piano Playing Music Based on Convolutional Cyclic Hashing Method

Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation

Construction of a digital library of vocal music resources based on second-order data decomposition algorithm

Contact Info

Product

Resources

About