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<p>Real-time music improvisation demands continuous audio effect adjustment, yet human control is limited by latency and cognitive load. Conventional model- or rule-based controllers struggle with cross-modal data fusion, often producing suboptimal trajectories. A dynamic architecture addresses this by analyzing multimodal cues. This research proposes a Music Effect Control with Chaotic Wing Suit Flying Search optimized-Seq2Seq (MEC-CWFSO-SeqNet) model, integrating attention-enhanced sequence modeling with a multi-layered MEC-CWFSO engine for adaptive hyperparameter tuning and output refinement. Multimodal inputs, including audio signals and motion sensor data, are processed using an encoder-decoder architecture. During training, the MEC-CWFSO-SeqNet algorithm iteratively adjusts latent embeddings to enhance predictive performance. A diverse dataset of expressive musical improvisations, incorporating instrumental and sensor-enhanced performance, is employed with high temporal resolution to ensure accurate multimodal alignment. Data augmentation techniques such as time stretching, pitch shifting, and Z-score normalization are applied to create balanced, key-agnostic sequences. Chroma features extracted via STFT and CQT were combined with performance and sensor data into 256-dimensional vectors. Transformer-based encoder layers model cross-domain relationships, while the attention-driven decoder predicts effect parameters. CWFSO dynamically optimizes decoder behavior and loss weights through chaotic search. MEC-CWFSO-SeqNet achieved improved performance with PF (+ 0.05), SPD (-0.31), ADP (-0.04), NDG (0.0033), PCG (0.017), and an accuracy of 98.8% compared to existing methods. Simulation-based measurements indicate an average latency of 46&#xa0;ms per frame on Intel Core i9 CPU, below the ~ 100&#xa0;ms perceptual threshold for live musical improvisation, demonstrating that attention-based sequence learning with chaotic meta-heuristic search enables a robust, timely expressive effect controller.</p>

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Simulation-based deep learning for IoT-oriented music improvisation optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月19日

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  • Meng Liu

摘要

Real-time music improvisation demands continuous audio effect adjustment, yet human control is limited by latency and cognitive load. Conventional model- or rule-based controllers struggle with cross-modal data fusion, often producing suboptimal trajectories. A dynamic architecture addresses this by analyzing multimodal cues. This research proposes a Music Effect Control with Chaotic Wing Suit Flying Search optimized-Seq2Seq (MEC-CWFSO-SeqNet) model, integrating attention-enhanced sequence modeling with a multi-layered MEC-CWFSO engine for adaptive hyperparameter tuning and output refinement. Multimodal inputs, including audio signals and motion sensor data, are processed using an encoder-decoder architecture. During training, the MEC-CWFSO-SeqNet algorithm iteratively adjusts latent embeddings to enhance predictive performance. A diverse dataset of expressive musical improvisations, incorporating instrumental and sensor-enhanced performance, is employed with high temporal resolution to ensure accurate multimodal alignment. Data augmentation techniques such as time stretching, pitch shifting, and Z-score normalization are applied to create balanced, key-agnostic sequences. Chroma features extracted via STFT and CQT were combined with performance and sensor data into 256-dimensional vectors. Transformer-based encoder layers model cross-domain relationships, while the attention-driven decoder predicts effect parameters. CWFSO dynamically optimizes decoder behavior and loss weights through chaotic search. MEC-CWFSO-SeqNet achieved improved performance with PF (+ 0.05), SPD (-0.31), ADP (-0.04), NDG (0.0033), PCG (0.017), and an accuracy of 98.8% compared to existing methods. Simulation-based measurements indicate an average latency of 46 ms per frame on Intel Core i9 CPU, below the ~ 100 ms perceptual threshold for live musical improvisation, demonstrating that attention-based sequence learning with chaotic meta-heuristic search enables a robust, timely expressive effect controller.