cs.SD · 2026-06-11 · No. 20

Sound, 2026-06-11.

6 new papers in cs.SD. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    PianoKontext: Expressive Performance Rendering from Deadpan Context

    Dmitrii Gavrilev

    cs.SD · cs.LG

    Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained...

    arxiv.org/abs/2606.12282 · PDF

  2. 02

    Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification

    Hemansh Shridhar, Miika Toikkanen, June-Woo Kim

    cs.SD · cs.AI

    Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST). While effective at modeling global context, recent analyses suggest a low-pass filtering behavior that may reduce sensitivity to localized abnormal patterns. In this work, we investigate State Space Models (SSMs) as an alternative backbone for RSC. Using the Distilled Audio State...

    arxiv.org/abs/2606.11922 · PDF

  3. 03

    Quality Adaptive Angular Margin Learning for Respiratory Sound Classification

    Yoon Tae Kim, Heejoon Koo, Miika Toikkanen, June-Woo Kim

    cs.SD · cs.AI

    We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability. Our framework, titled QLung, introduces a no-reference audio quality margin derived from spectral entropy and root-mean-square energy, which adaptively scales angular margins based on recording quality. To this end, we propose a log-scaled angular margin that stabilizes...

    arxiv.org/abs/2606.11915 · PDF

  4. 04

    Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering

    Haoning Xu, Zhaoqing Li, Huimeng Wang, Youjun Chen, Chengxi Deng, Mengzhe Geng, Xunying Liu

    cs.SD · cs.AI · eess.AS

    This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means. More fine-grained, mixed sparsity pruning by layer-level varying number of parameter clusters is also explored. Experiments conducted on the LibriSpeech dataset suggest that when operating with pruning sparsity of 50% on HuBERT-large, consistent WER reductions of 27.73%/18.61% absolute...

    arxiv.org/abs/2606.11836 · PDF

  5. 05

    Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions

    Haiyun Li, Shuhai Peng, Zhisheng Zhang, Jingran Xie, Xiaofeng Xie, Hanyang Peng, Zhiyong Wu

    cs.SD · cs.AI · cs.CR · cs.MM

    Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models. Improving robustness is challenging due to the inherent robustness-fidelity trade-off in existing designs, where increasing watermark energy improves robustness...

    arxiv.org/abs/2606.11828 · PDF

  6. 06

    SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing

    Anton Firc, Vojtěch Staněk, Zbyněk Lička, Kamil Malinka, Martin Perešíni

    cs.SD · cs.LG

    We present SpAArSIST, a deployment-oriented refinement of the widely used AASIST graph pooling backend for self-supervised learning (SSL) based anti-spoofing. Motivated by redundant operations in public implementations, we replace learned pooling and stack-node attention with explicit, lightweight choices: separate train and inference graph pooling ratios $(k_{\mathrm{tr}},k_{\mathrm{inf}})$, magnitude-based node scoring, and mean aggregation...

    arxiv.org/abs/2606.11674 · PDF

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