arXiv subjects

Adi Arbel

Publications and source records attributed to Adi Arbel.

3 recordsLinked to original sources

arXiv2026

Complex Interpolation of Matrices with an application to Multi-Manifold Learning

Given two symmetric positive-definite matrices $A, B \in \mathbb{R}^{n \times n}$, we study the spectral properties of the interpolation $A^{1-x} B^x$ for $0 \leq x \leq 1$. The presence of `common structures' in $A$ and $B$, eigenvectors pointing in a similar direction, can be investigated using this interpolation perspective. Generically, exact log-linearity of the operator norm $\|A^{1-x} B^x\|$ is equivalent to the existence of a shared eigenvector in the original matrices; stability bounds show that approximate log-linearity forces principal singular vectors to align with leading eigenvectors of both matrices. These results give rise to and provide theoretical justification for a multi-manifold learning framework that identifies common and distinct latent structures in multiview data.

cs.LG↗

arXiv2026

DiT-Flow: Speech Enhancement Robust to Multiple Distortions based on Flow Matching in Latent Space and Diffusion Transformers

Recent advances in generative models, such as diffusion and flow matching, have shown strong performance in audio tasks. However, speech enhancement (SE) models are typically trained on limited datasets and evaluated under narrow conditions, limiting real-world applicability. To address this, we propose DiT-Flow, a flow matching-based SE framework built on the latent Diffusion Transformer (DiT) backbone and trained for robustness across diverse distortions, including noise, reverberation, and compression. DiT-Flow operates on compact variational auto-encoders (VAEs)-derived latent features. We validated our approach on StillSonicSet, a synthetic yet acoustically realistic dataset composed of LibriSpeech, FSD50K, FMA, and 90 Matterport3D scenes. Experiments show that DiT-Flow consistently outperforms state-of-the-art generative SE models, demonstrating the effectiveness of flow matching in multi-condition speech enhancement. Despite ongoing efforts to expand synthetic data realism, a persistent bottleneck in SE is the inevitable mismatch between training and deployment conditions. By integrating LoRA with the MoE framework, we achieve both parameter-efficient and high-performance training for DiT-Flow robust to multiple distortions with using 4.9% percentage of the total parameters to obtain a better performance on five unseen distortions.

eess.AS↗

arXiv2025

ReFESS-QI: Reference-Free Evaluation For Speech Separation With Joint Quality And Intelligibility Scoring

Source separation is a crucial pre-processing step for various speech processing tasks, such as automatic speech recognition (ASR). Traditionally, the evaluation metrics for speech separation rely on the matched reference audios and corresponding transcriptions to assess audio quality and intelligibility. However, they cannot be used to evaluate real-world mixtures for which no reference exists. This paper introduces a text-free reference-free evaluation framework based on self-supervised learning (SSL) representations. The proposed framework utilize the mixture and separated tracks to predict jointly audio quality, through the Scale Invariant Signal to Noise Ratio (SI-SNR) metric, and speech intelligibility through the Word Error Rate (WER) metric. We conducted experiments on the WHAMR! dataset, which shows a WER estimation with a mean absolute error (MAE) of 17% and a Pearson correlation coefficient (PCC) of 0.77; and SI-SNR estimation with an MAE of 1.38 and PCC of 0.95. We further demonstrate the robustness of our estimator by using various SSL representations.

eess.AS↗