MADS (Multi-view Acoustic Descriptor Set) is a compact 19‑dimensional, physics‑informed descriptor set designed to capture spectral, temporal, mechanical, and stochastic aspects of audio signals. Unlike traditional log‑mel or MFCC representations, MADS encodes excitation, damping, periodicity, impulsiveness, and structural consistency in a unified multi‑view format. Evaluated on ESC‑10, ESC‑50, and MSoS datasets with classical machine learning models, MADS outperforms conventional 26‑D MFCC and 38‑D spectral‑summary baselines, achieving 81.00% on ESC‑10, 52.78% on ESC‑50, and 67.48% on MSoS while using roughly half the dimensionality of the 38‑D baseline.
Dominant audio classification pipelines rely either on compact handcrafted summaries or on fixed time-frequency frontends such as log-mel representations prior to deep modeling. While highly successfu...
arXiv:2607. 03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood.
arXiv:2511. 21325v2 Announce Type: replace-cross Abstract: Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs.
arXiv:2608. 13817v1 Announce Type: cross Abstract: Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals.
UniAE-MoE is a unified audio encoder that uses a Mixture-of-Experts architecture to model cross‑domain audio representations. It integrates encoder components from Qwen2‑Audio and Audio‑Flamingo 3, enhances them with SwiGLU and shared experts, and applies a two‑stage instruction‑tuning strategy along with task‑specific data scaling. The model achieves state‑of‑the‑art results on the XARES‑LLM benchmark (0.802) and tops the Interspeech 2026 Audio Encoder Capability Challenge, demonstrating strong generalization across speech, music, and general audio tasks.
The paper proposes a hierarchical conditioning approach for joint Acoustic Scene Classification and Audio Tagging, predicting scene categories directly from intermediate event probabilities. Extensive experiments on three backbones and varied loss‑weighting settings show that this method matches or surpasses traditional multi‑head models while enabling synchronized dual‑task learning for certain beta values. An interpretability framework combining information bottleneck analysis, manifold geometry, and Formal Concept Analysis on TF‑IDF event representations demonstrates that hierarchical conditioning reduces conditional entropy, increases mutual information, and improves latent cluster separability, yielding a more structured and semantically faithful feature space.
AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.
arXiv:2606. 11922v1 Announce Type: cross Abstract: Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST).
arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.
arXiv:2606. 04210v1 Announce Type: cross Abstract: Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added.
arXiv:2607. 05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech.
arXiv:2607. 08545v1 Announce Type: cross Abstract: End-to-end neural audio models achieve high-fidelity compression and generation.