<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the increasing importance of music information retrieval, the construction of effective music recognition methods has gradually become one of the research focuses. The vocal map features of the collected music pronunciation signals are extracted. The research in this paper is primarily based on the basic physical characteristics of music notes; once these characteristics are identified, then they are mathematically extracted and analysed (voiceprint feature method), and a recognition model is established. On this basis, the process of adaptive separation and recognition of music signals is completed. Finally, the performance of different recognition types is verified and evaluated through experiments. The results show that the average accuracy rate of the modified algorithm within a certain range reaches 73.6%; additionally, the average accuracy rate is increased by 10.95% compared with the audio recognition based on Internet of Things (IoT) data, and is more accurate than the audio recognition method based on data collection, showing an improvement of 20.75%. This shows that the modified recognition algorithm adopted in the present research has stable and high accuracy for comprehensive music types with different characteristics. Finally, the identification method proposed in this paper shortens the time by 85.71% compared with the identification method of data collection, and shortens the identification time by 83.33% compared with the IoT identification method. This greatly improves the recognition of different musical feature types.</p>
</abstract>

With the increasing importance of music information retrieval, the construction of effective music recognition methods has gradually become one of the research focuses. The vocal map features of the collected music pronunciation signals are extracted. The research in this paper is primarily based on the basic physical characteristics of music notes; once these characteristics are identified, then they are mathematically extracted and analysed (voiceprint feature method), and a recognition model is established. On this basis, the process of adaptive separation and recognition of music signals is completed. Finally, the performance of different recognition types is verified and evaluated through experiments. The results show that the average accuracy rate of the modified algorithm within a certain range reaches 73.6%; additionally, the average accuracy rate is increased by 10.95% compared with the audio recognition based on Internet of Things (IoT) data, and is more accurate than the audio recognition method based on data collection, showing an improvement of 20.75%. This shows that the modified recognition algorithm adopted in the present research has stable and high accuracy for comprehensive music types with different characteristics. Finally, the identification method proposed in this paper shortens the time by 85.71% compared with the identification method of data collection, and shortens the identification time by 83.33% compared with the IoT identification method. This greatly improves the recognition of different musical feature types.

Simulation research on music feature recognition based on mobile big data and smart sensors

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the increasing importance of music information retrieval, the construction of effective music recognition methods has gradually become one of the research...

Simulation research on music feature recognition based on mobile big data and smart sensors

Published Online: Apr 10, 2023

Page range: 1117 - 1128

Received: Jun 08, 2022

Accepted: Aug 08, 2022

DOI: https://doi.org/10.2478/amns.2021.2.00265

Keywords
feature recognition, music recognition, big data, signal processing

© 2023 Wen An, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Simulation research on music feature recognition based on mobile big data and smart sensors

Published Online: Apr 10, 2023

Page range: 1117 - 1128

Received: Jun 08, 2022

Accepted: Aug 08, 2022

DOI: https://doi.org/10.2478/amns.2021.2.00265

Keywordsfeature recognition, music recognition, big data, signal processing

© 2023 Wen An, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
feature recognition, music recognition, big data, signal processing