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Aalborg University

Academic institutioneurope · dk
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Research library493linked papers
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Selected work

Representative Papers

An approach to melodic segmentation and classification based on filtering with the Haar-wavelet

Dec 01, 2013

This work addresses automatic segmentation and classification of symbolic melodies, specifically targeting two tasks: attribution of excerpts from Bach’s Two-Part Inventions (BWV 772–786) to their parent works, and assignment of 360 Dutch folk songs to one of 26 tune families. We propose a single-scale continuous Haar wavelet filtering method that models pitch sequences as time-series signals; segmentation is driven by detection of local extrema and zero crossings, followed by k-nearest neighbors classification using Euclidean or Manhattan distance. This is the first application of Haar wavelets directly for melodic structural representation and segmentation—bypassing heuristic Gestalt-based rules and enabling cross-modal integration of signal processing and music cognition. Experiments show our method significantly outperforms both unfiltered pitch sequences and Gestalt-based segmentation on Bach excerpt attribution, and achieves performance close to the pitch-based baseline on folk tune family classification—slightly below state-of-the-art multi-feature string-matching approaches.

32 citationsRead paper

Towards Open Diversity-Aware Social Interactions

Feb 17, 2025arXiv.org

In the digital era, the rapid proliferation of diverse populations, perspectives, and knowledge lacks corresponding adaptive mechanisms, leading to superficial social relationships and intensified echo chambers. Method: This study proposes and implements the “We Internet” platform, introducing— for the first time—the Diversity-Aware AI framework, which integrates sociology, ethics, and artificial intelligence. It establishes multidimensional modeling and representation learning methods for social diversity and designs a human-AI collaborative, ethics-driven algorithmic architecture with interpretable matching guidance. Contribution/Results: Empirical validation demonstrates that the framework significantly enhances cross-group understanding, mitigates filter bubbles, and deepens collaborative engagement. It provides both a theoretical foundation and an implementable paradigm for open, inclusive, and trustworthy social AI systems.

3 citationsRead paper

Monitoring Timed Properties (Revisited)

Jun 29, 2022International Conference on Formal Modeling and Analysis of Timed Systems

This work addresses the challenge of online monitoring for real-time systems, where temporal properties are specified in Metric Interval Temporal Logic (MITL) and recognized by Timed Büchi Automata (TBA). We propose an efficient symbolic online monitoring method grounded in zone-based representation. To handle timing uncertainty, we introduce, for the first time, a time-divergence simplification mechanism; additionally, we design a minimum-time estimation strategy enabling early conclusive verdicts. Compared to conventional approaches, our method significantly improves monitoring efficiency and robustness—achieving low-overhead, high-accuracy online decision-making and predictive judgment across diverse real-time scenarios. The framework advances formal monitoring for uncertain real-time environments by unifying symbolic reasoning with proactive timing analysis, establishing a novel paradigm for runtime verification under timing imprecision.

3 citationsRead paper

Wavelet-filtering of symbolic music representations for folk tune segmentation and classification

Jun 05, 2013

This work addresses the automatic segmentation and tune-family classification of folk-song symbolic scores. We propose a multi-scale time-frequency analysis method based on the Haar continuous wavelet transform (CWT). Symbolic scores are modeled as discrete pitch–time sequences; structural features are extracted via Haar wavelet filtering, and phrase boundaries are precisely localized using local maxima of the wavelet coefficients. Tune-family identification is performed using a k-nearest neighbors classifier with either Euclidean or Manhattan distance. To our knowledge, this is the first application of Haar wavelet filtering to symbolic music segmentation and classification, significantly enhancing melodic structural perception and classification robustness. Cross-validation results demonstrate that the optimized scale-parameter configuration achieves substantially higher classification accuracy than conventional Gestalt-based baselines.

3 citationsRead paper

Autonomous Microscopy Experiments through Large Language Model Agents

Dec 18, 2024arXiv.org

Existing self-driving laboratories (SDLs) rely on static experimental protocols, limiting their ability to emulate scientists’ adaptive reasoning and intuition in dynamic environments. Method: We propose AILA, the first large language model (LLM)-based autonomous agent system for end-to-end atomic force microscopy (AFM) experimentation—encompassing experimental design, execution, analysis, and closed-loop decision-making. Contribution/Results: We introduce AFMBench, the first benchmark for evaluating LLMs in AFM-driven scientific discovery, uncovering critical deficiencies in multi-agent coordination (73% failure rate), instruction following, and safety alignment, while empirically delineating LLMs’ scientific reasoning boundaries. Leveraging task-decomposition prompting, hardware interface integration, and a multi-agent architecture, AILA achieves autonomous AFM calibration, high-resolution feature identification, and nanomechanical property quantification. Results further reveal substantial accuracy degradation in foundational tasks (e.g., document retrieval), underscoring robustness and trustworthiness as central challenges in AI for Science.

2 citationsRead paper
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