Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.18999

Variable-Rate Harmonic-Percussive Time-Scale Modification with Real-Time Playback in Python

Sayema Lubis·Clark Peng·Jared Carreño·TJ Tsai
View original source ↗Read source document ↗Export metadata ↓

Abstract

Time-scale modification (TSM) has a number of open-source implementations, but these are designed almost exclusively for offline use, in which a recording is processed at a fixed rate and written out ahead of time. Applications such as automatic musical accompaniment require a different setting, which we call variable-rate playback: the recording to be stretched is known in advance, but the playback rate is not, and must change continuously in response to a live performer. The few implementations that generate output in real time are written in C++ and optimized for speed rather than for ease of modification, experimentation, and integration with the primarily Python-based research ecosystem. This paper describes a Python implementation of the widely used harmonic-percussive TSM method for the variable-rate playback setting. Harmonic-percussive separation is performed offline as a preprocessing step on the known input recording, while synthesis and playback are carried out in real time with a time-scale factor that may change at every frame. We further propose a family of variants that reduce runtime by replacing the phase vocoder's analysis-stage FFT and instantaneous frequency calculations with lookups into precomputed tables. Subjective listening tests with 24 participants (1114 pairwise ratings) show that these approximations become perceptually indistinguishable from the exact implementation once the precomputed hop size is sufficiently small, while reducing total runtime by roughly half. We characterize the resulting tradeoffs among precomputation, runtime, memory, and perceptual quality to guide algorithm selection, and we release our implementation as an open-source package.

Explore related subjects

cs.SDeess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sayema Lubis, Clark Peng, Jared Carreño, TJ Tsai. 2026-07-20. Variable-Rate Harmonic-Percussive Time-Scale Modification with Real-Time Playback in Python. https://arxiv.org/abs/2609.18999

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗
arXiv2026

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↗