Search arXiv⌕ Search

arXiv · 2601.12314

A Similarity Network for Correlating Musical Structure to Military Strategy

Abstract

Music perception, a multi-sensory process based on the synesthesia effect, is an essential component of music aesthetic education. Understanding music structure helps both perception and aesthetic education. Music structure incorporates a range of information, the coordination of which forms the melody, just as different military actions cooperate to produce a military strategy. However, there are a few ways for assessing music perception from the perspectives of system operation and information management. In this paper, we explore the similarities between music structure and military strategy while creating the Music Clips Correlation Network (MCCN) based on Mel-frequency Cepstral Coefficients (MFCCs). The inspiration comes from the comparison between a concert conductor's musical score and a military war commander's sand table exercise. Specifically, we create MCCNs for various kinds of war movie soundtracks, then relate military tactics (Sun Tzu's Art of War, etc.) and political institutions to military operations networks. Our primary findings suggest a few similarities, implying that music perception and aesthetic education can be approached from a military strategy and management perspective through this interdisciplinary research. Similarly, we can discover similarities between the art of military scheming and the art of musical structure based on network analysis in order to facilitate the understanding of the relationship between technology and art.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yiwen Zhang, Hui Zhang, Fanqin Meng. 2026-01-18. A Similarity Network for Correlating Musical Structure to Military Strategy. https://arxiv.org/abs/2601.12314

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Causal Tracing of Audio-Text Fusion in Large Audio Language Models

Despite the strong performance of large audio language models (LALMs) in various tasks, exactly how and where they integrate acoustic features with textual context remains unclear. We adapt causal tracing to investigate the internal information flow of LALMs during audio comprehension. By conducting layer-wise and token-wise analyses across DeSTA, Qwen, and Voxtral, we evaluate the causal effects of individual hidden states. Layer-wise analysis identifies different fusion strategies, from progressive integration in DeSTA to abrupt late-stage fusion in Qwen. Token-wise analysis shows that the final sequence token acts as an informational bottleneck where the network decisively retrieves relevant information from the audio. We also observe an attention-like query mechanism at intermediate token positions that triggers the model to pull task-relevant audio context. These findings provide a clear characterization of when and where multi-modal integration occurs within LALMs.

cs.SD↗

Sona: Personalized Soundscape Mediation to Support People with Sound Sensitivity

People with sound sensitivity (PWSS) often manage distressing sounds with earplugs and noise-canceling headphones that broadly suppress their surroundings, limiting access to useful auditory cues. We present Sona, a mobile system for personalized, real-time soundscape mediation, informed by prior sound sensitivity research and an online survey of 68 PWSS. Sona selectively attenuates multiple overlapping user-chosen sounds at adjustable strength, suggests targets from ambient sound recognition, and lets users add custom targets from short recordings without retraining the model. In an in-situ evaluation with ten PWSS, participants reported that Sona made their soundscapes more manageable. The study also surfaced uneven attenuation across sound type and context, tensions between managing filters and attending to ongoing activities, and difficulty interpreting personalization outcomes. These findings highlight the need to design for the quality of the residual soundscape, balance user control with interaction demands, and support guided, interpretable personalization.

cs.SD↗

FRAUDSkill: Structured Frozen-Weight Skill Optimization for Audio Anti-Fraud Detection

Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their deployment, however, requires predictions to follow a predefined label space and a structured decision protocol consisting of service-scenario identification, fraud detection, and conditional fraud-type classification. Existing fine-tuning and prompt-based approaches typically encode task knowledge, constraints, and decision rules into model parameters or manually maintained prompts, making them difficult to adapt as fraud patterns and labeling policies evolve. To this end, we propose FRAUDSkill, a structured frozen-weight adaptation framework that leaves the underlying audio-language model unchanged while optimizing an external layer of skill programs, route-specific policies, and decision rules. We further combine structured output control with validation-guided multi-path inference to ensure protocol-compliant predictions. On the TeleAntiFraud benchmark, FRAUDSkill achieves 73.50% Macro-F1, outperforming the shared frozen-model baseline by 31.96% while reducing invalid outputs to 1.94%. Extensive experiments demonstrate that external skill optimization provides an effective and adaptable solution for structured audio anti-fraud detection without modifying the underlying model. The source code is available at https://anonymous.4open.science/r/FRAUDSKILL-114514.

cs.SD↗