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<p>The integration of real-time music information retrieval techniques into musical instruments is a crucial step towards smart musical instruments that can reason about the musical context. This paper presents a real-time guitar playing technique recognition system for a smart electro-acoustic guitar. The proposed system comprises a software recognition pipeline running on a Raspberry Pi 4 and is designed to listen to the guitar’s audio signal and classify each note into eight playing techniques, both pitched and percussive. Real-time playing technique information is used in real-time to allow the musician to control wirelessly-connected stage equipment during performance. The recognition pipeline includes an onset detector, feature extractors, and a convolutional neural classifier. Four pipeline configurations are proposed, striking different balances between accuracy and sound-to-result latency. Results show how optimal performance improvements occur when latency constraints are increased from 15 to 45 ms, with performance varying between pitched and percussive techniques based on available audio context. Our findings highlight the challenges of generalization across players and instruments, demonstrating that accurate recognition requires substantial datasets and carefully selected cross-validation strategies. The research also reveals how individual player styles significantly impact technique recognition performance.</p>

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Real-time playing technique recognition embedded in a smart acoustic guitar

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月17日

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  • Domenico Stefani,
  • Luca Turchet

摘要

The integration of real-time music information retrieval techniques into musical instruments is a crucial step towards smart musical instruments that can reason about the musical context. This paper presents a real-time guitar playing technique recognition system for a smart electro-acoustic guitar. The proposed system comprises a software recognition pipeline running on a Raspberry Pi 4 and is designed to listen to the guitar’s audio signal and classify each note into eight playing techniques, both pitched and percussive. Real-time playing technique information is used in real-time to allow the musician to control wirelessly-connected stage equipment during performance. The recognition pipeline includes an onset detector, feature extractors, and a convolutional neural classifier. Four pipeline configurations are proposed, striking different balances between accuracy and sound-to-result latency. Results show how optimal performance improvements occur when latency constraints are increased from 15 to 45 ms, with performance varying between pitched and percussive techniques based on available audio context. Our findings highlight the challenges of generalization across players and instruments, demonstrating that accurate recognition requires substantial datasets and carefully selected cross-validation strategies. The research also reveals how individual player styles significantly impact technique recognition performance.