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Zhejing Hu

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

Is Symbolic Music a Specific Language? Exploring Inspiration-to-Structure Machine Composition via LLMs

Zhejing Hu
Yan Liu
Zhi Zhang
Aiwei Zhang
Sheng-hua Zhong
Bruce X.B. Yu
Gong Chen

Large Language Models (LLMs) have demonstrated remarkable proficiency in diverse tasks. This success raises a fundamental question in machine composition: Can symbolic music be considered a special form of language that can be jointly modeled with natural language for composition tasks? Recent studies validate that symbolic music can be modeled as a human language, yet composing structured music from partial symbolic inputs through natural language interaction remains underexplored. Even LLMs struggle to generate structurally coherent compositions in such hybrid input-output scenarios, highlighting a fundamental gap that calls for a domain-specific learning paradigm. To this end, we propose Inspiration-to-Structure (IoS), a cognitively inspired framework that enables LLMs to generate structured musical sections from melodic ideas. IoS employs a three-phase process—semantic, structural, and collaborative cognition—and is supported by two key components: (1) a new dataset and construction protocol called Structured Triplet Data (STD), and (2) a training method, Dual-Instance Structural Contrastive Optimization (DiSCO), designed to enhance structural awareness. Experiments show that IoS improves structural coherence by 47.8% and artistic creativity by 21.8% compared to conventional language modeling paradigm, supervised fine-tuning, and even enables smaller LLMs to surpass larger LLMs. These results suggest that symbolic music, while language-like, demands specialized modeling beyond standard language modeling paradigms. IoS enables LLMs to transform music theory knowledge into structured composition, empowering users to compose music interactively via language and advancing toward general creative AI.

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IJCAI Conference 2025 Conference Paper

CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation

Zhejing Hu
Yan Liu
Gong Chen
Bruce X. B. Yu

Generative artificial intelligence in music has made significant strides, yet it still falls short of the substantial achievements seen in natural language processing, primarily due to the limited availability of music data. Knowledge-informed approaches have been shown to enhance the performance of music generation models, even when only a few pieces of musical knowledge are integrated. This paper seeks to leverage comprehensive music theory in AI-driven music generation tasks, such as algorithmic composition and style transfer, which traditionally require significant manual effort with existing techniques. We introduce a novel automatic music lexicon construction model that generates a lexicon, named CompLex, comprising 37, 432 items derived from just 9 manually input category keywords and 5 sentence prompt templates. A new multi-agent algorithm is proposed to automatically detect and mitigate hallucinations. CompLex demonstrates impressive performance improvements across three state-of-the-art text-to-music generation models, encompassing both symbolic and audio-based methods. Furthermore, we evaluate CompLex in terms of completeness, accuracy, non-redundancy, and executability, confirming that it possesses the key characteristics of an effective lexicon.

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AAAI Conference 2025 Conference Paper

Compose with Me: Collaborative Music Inpainter for Symbolic Music Infilling

Zhejing Hu
Yan Liu
Gong Chen
Bruce X.B. Yu

The field of music generation has seen a surge of interest from both academia and industry, with innovative platforms such as Suno, Udio, and SkyMusic earning widespread recognition. However, the challenge of music infilling—modifying specific music segments without reconstructing the entire piece—remains a significant hurdle for both audio-based and symbolic-based models, limiting their adaptability and practicality. In this paper, we address symbolic music infilling by introducing the Collaborative Music Inpainter (CMI), an advanced human-in-the-loop (HITL) model for music infilling. The CMI features the Joint Embedding Predictive Autoregressive Generative Architecture (JEP-AGA), which learns the high-level predictive representations of the masked part that needs to be infilled during the autoregressive generative process, akin to how humans perceive and interpret music. The newly developed Dynamic Interaction Learner (DIL) achieves HITL by iteratively refining the infilled output based on user interactions alone, significantly reducing the interaction cost without requiring further input. Experimental results confirm CMI’s superior performance in music infilling, demonstrating its efficiency in producing high-quality music.

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AAAI Conference 2024 Conference Paper

Responding to the Call: Exploring Automatic Music Composition Using a Knowledge-Enhanced Model

Zhejing Hu
Yan Liu
Gong Chen
Xiao Ma
Shenghua Zhong
Qianwen Luo

Call-and-response is a musical technique that enriches the creativity of music, crafting coherent musical ideas that mirror the back-and-forth nature of human dialogue with distinct musical characteristics. Although this technique is integral to numerous musical compositions, it remains largely uncharted in automatic music composition. To enhance the creativity of machine-composed music, we first introduce the Call-Response Dataset (CRD) containing 19,155 annotated musical pairs and crafted comprehensive objective evaluation metrics for musical assessment. Then, we design a knowledge-enhanced learning-based method to bridge the gap between human and machine creativity. Specifically, we train the composition module using the call-response pairs, supplementing it with musical knowledge in terms of rhythm, melody, and harmony. Our experimental results underscore that our proposed model adeptly produces a wide variety of creative responses for various musical calls.

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AIIM Journal 2020 Journal Article

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques

Fen Miao
Bo Wen
Zhejing Hu
Giancarlo Fortino
Xi-Ping Wang
Zeng-Ding Liu
Min Tang
Ye Li

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous BP estimation approach using one-channel ECG signals for unobtrusive BP monitoring. A BP model is developed based on the fusion of a residual network and long short-term memory to obtain the spatial-temporal information of ECG signals. The public multiparameter intelligent monitoring waveform database, which contains ECG, PPG, and invasive BP data of patients in intensive care units, is used to develop and verify the model. Experimental results demonstrated that the proposed approach exhibited an estimation error of 0. 07 ± 7. 77 mmHg for mean arterial pressure (MAP) and 0. 01 ± 6. 29 for diastolic BP (DBP), which comply with the Association for the Advancement of Medical Instrumentation standard. According to the British Hypertension Society standards, the results achieved grade A for MAP and DBP estimation and grade B for systolic BP (SBP) estimation. Furthermore, we verified the model with an independent dataset for arrhythmia patients. The experimental results exhibited an estimation error of −0. 22 ± 5. 82 mmHg, −0. 57 ± 4. 39 mmHg, and −0. 75 ± 5. 62 mmHg for SBP, MAP, and DBP measurements, respectively. These results indicate the feasibility of estimating BP by using a one-channel ECG signal, thus enabling continuous BP measurement for ubiquitous health care applications.

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