SearcharxivSearch

arXiv · 2506.08540

Higher-Order Network Representation of J. S. Bach's Solo Violin Sonatas and Partitas: Topological and Geometrical Explorations

Abstract

Music is inherently complex, with structures and interactions that unfold across multiple layers. Complex networks have emerged as powerful structures for the quantitative analysis of Western classical music, revealing significant features of its harmonic and structural organization. Although notable works have used these approaches to study music, dyadic representations of interactions fall short in conveying the underlying complexity and depth. In recent years, the limitations of traditional graph representations have been questioned and challenged in the context of interactions that could be higher-dimensional. Effective musical analysis requires models that capture higher-order interactions and a framework that simultaneously captures transitions between them. Subsequently, in this paper, we present a topological framework for analyzing J. S. Bach's Solo Violin Sonatas and Partitas that uses higher-order networks where single notes are vertices, two-note chords are edges, three-notes are triangles, etc. We subsequently account for the flow of music, by modeling transitions between successive notes. We identify genre-specific patterns in the works' geometric and topological properties. In particular, we find signatures in the trends of the evolution of the Euler characteristic and curvature, as well as examining adherence to the Gauss-Bonnet theorem across different movement types. The distinctions are revealed between slow movements, Fugues, and Baroque dance movements through their simplicial complex representation.

Explore related subjects

Keep this discovery

BibTeXRIS

Dima Mrad, Sara Najem. 2025-06-10. Higher-Order Network Representation of J. S. Bach's Solo Violin Sonatas and Partitas: Topological and Geometrical Explorations. https://arxiv.org/abs/2506.08540

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

KEEP EXPLORING

Related papers

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD