CaReCoS: A Spectrogram based Visual Benchmark for Cardiac, Respiratory and Cough Sounds

Medical acoustic signals such as respiratory sounds, cardiac auscultations, and cough audio carry rich diagnostic information, yet no existing benchmark evaluates multimodal reasoning over their spectrogram representations. We address both gaps with CaReCoS, a benchmark pairing clinically grounded questions with mel-spectrogram images derived from seven medical audio datasets. Evaluating 9 state-of-the-art vision and omni models, we find that all struggle with fine-grained acoustic features encoded in spectrograms: no model reliably combines visual pattern recognition with medical knowledge, achieving a maximum accuracy of 51.2%, underscoring the need for training on medical sound visualizations.

A Variational-Flow Analysis of StoRM under Noise-Power Mismatch

Diffusion-based speech enhancement architectures that pair a deterministic predictor with a learned score network, exhibit a sharp non-smooth transition (``kink'') in the SI-SDR degradation curve at the training-time noise amplitude. We give a pathwise variational-flow analysis that localizes this non-smoothness to the predictor stage. The central identity is an exact factorization of the parametric sensitivity, $\partial \sig^{(M)} / \partial M = K(M) \cdot \partial C_M / \partial M$, where $K(M)$ is a continuous matrix-valued functional of the score Jacobian along the reverse trajectory and $C_M = Π(y^{(M)})$ is the predictor output. Under three hypotheses on the reverse-process flow (score-Jacobian continuity, conditioning-Jacobian continuity, non-degeneracy of $K$), failure of $M \mapsto \sig^{(M)}$ to be $C^1$ at $M^\ast$ holds if and only if $M \mapsto Π(y^{(M)})$ fails to be $C^1$ at $M^\ast$. We extend the localization to the finite-step Euler--Maruyama sampler actually run at inference. The hypotheses translate into a concrete experimental program; this paper specifies the program and presents the variational structure. The empirical validation is deferred to a companion experimental report.

Bridging Self-Supervised Learning and Speech Enhancement: A Wav2Vec2-Conditioned Framework

Diffusion models show potential for speech enhancement but lack linguistic guidance. We condition a diffusion-based model on wav2vec 2.0 features from noisy input, injected at the U-Net bottleneck via Feature-wise Linear Modulation (FiLM). Phonetic representations from wav2vec 2.0 features of degraded speech, anchor the reverse diffusion process. While a frozen wav2vec 2.0 encoder extracts features, a learned FiLM generator produces scale and shift parameters modulating the bottleneck with minimal overhead. Motivated by the optimal Bayesian causal estimator under a linear-Gaussian state-space model, FiLM coefficients are aggregated via exponential smoothing for temporal compression. Evaluation on VoiceBank-DEMAND and LibriMix shows competitive performance against the unconditioned baseline in PESQ, STOI, SI-SDR and DNSMOS. We consistently record an improvement of 0.4 on PESQ score, suggesting self-supervised representations effectively condition diffusion-based speech enhancement.