arXiv AI August 13, 2026 By Oshan A. B. Yalegama, Wageesha N. Manamperi

Deep Learning Based Relative Transfer Matrix Estimation for Multiple Sources and Multiple Microphones

arXiv:2608. 11627v1 Announce Type: cross Abstract: The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, shows promising performance when applied to speech enhancement in noisy environments.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 7

Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics

arXiv:2607. 04471v1 Announce Type: cross Abstract: Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization.

By Jakob Kienegger, Tal Peer, Sina Khanagha, Timo Gerkmann

arXiv AI

Jun 18

NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization

arXiv:2606. 18664v1 Announce Type: cross Abstract: Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effectively in dynamic environments.

By Yizhuo Yang, Junqiao Fan, Shenghai Yuan, Lihua Xie

agents robotics

arXiv AI

Jun 9

MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation

arXiv:2606. 09677v1 Announce Type: cross Abstract: While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality.

By Dohwan Kim, Jung-Woo Choi

benchmarks

arXiv Machine Learning

Jul 3

CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging

arXiv:2607. 01295v1 Announce Type: cross Abstract: Acoustic imaging visualization is a core methodology in acoustics, enabling spatial analysis of sound sources and acoustic scenes.

By Marianthi Adamopoulou, Parthasaarathy Sudarsanam, David Diaz-Guerra, Meng Jiang, Archontis Politis, Seyed Jalaleddin Mousavirad, Tuomas Virtanen, Jan Lundgren

arXiv AI

1d ago

Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer

arXiv:2608. 15690v1 Announce Type: cross Abstract: Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output.

By Ivan Mikheev, Viacheslav Vasilev, Anna Dmitrienko, Alexey Letunovskiy, Ivan Kirillov, Kirill Chernyshev, Denis Dimitrov

rag diffusion fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jun 17

NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization

Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effectively in dynamic environments. Classical methods such as Multiple Signal Classification (MUSIC) offer strong theoretical foundations but degrade under low signal-to-noise ratios.

agents robotics

Related stories

Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics

NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization

MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation

CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging

Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer

NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization