{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T05:44:19Z","timestamp":1768196659268,"version":"3.49.0"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2026,3,30]]},"abstract":"<jats:p>Background: Chord perception is fundamental to automatic music annotation and is an essential tool for analyzing music structure and tuning. This study leverages the basic theory of cognitive psychology in music education to achieve real-time chord perception using Artificial Neural Network (ANN) technology. Methodology: The process begins with time-frequency transformation using the Constant Q Transform (CQT). This step is crucial for converting the audio signal into a format that can be effectively analyzed. To enhance the robustness of the system, several techniques are employed, including initial point detection, intonation, and benchmark analysis. These methods help in accurately identifying and processing the chords. A deep learning algorithm based on the Long-Short-Term Memory Network (LSTM) is constructed using Recurrent Neural Networks (RNNs). Additionally, a Monitor Mechanism is introduced to further improve the performance and reliability of the LSTM model. The Monitor Mechanism is integrated into the LSTM-based RNN to provide a feedback loop that enhances the model\u2019s ability to learn and adapt. This mechanism ensures that the system can continuously monitor and adjust its performance, leading to more accurate and reliable chord perception. Results and Discussion: The proposed method has demonstrated significant educational benefits, achieving over 60% effectiveness for educators within a relatively short period. Compared to existing methods, this approach has shown superior results, making it a valuable tool in the field of music creation and education. Conclusion: This paper highlights the effectiveness of artificial intelligence methods in the domain of music creation and education. The use of ANN, specifically the LSTM-based RNN with a Monitor Mechanism, provides a robust and efficient solution for real-time chord perception, enhancing both the analytical and educational aspects of music.<\/jats:p>","DOI":"10.1142\/s0218126625504675","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T10:29:20Z","timestamp":1755858560000},"source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Chord Perception Using LSTM-Based RNN with Monitor Mechanism for Music Education and Analysis"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6741-106X","authenticated-orcid":false,"given":"Xingping","family":"Yu","sequence":"first","affiliation":[{"name":"School of Music and Dance, Huaqiao University, Xiamen 361021, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6907-3322","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"S0218126625504675BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2013.11.005"},{"key":"S0218126625504675BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.09.009"},{"key":"S0218126625504675BIB003","author":"Tyroll S. B.","year":"2021","journal-title":"Sound and Music Computing Conference"},{"key":"S0218126625504675BIB004","doi-asserted-by":"publisher","DOI":"10.1080\/17459737.2023.2197905"},{"key":"S0218126625504675BIB005","first-page":"1","volume":"20","author":"Shirazi M.","year":"2011","journal-title":"SSRN Electron. 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