arXiv AI
Jun 17
Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
arXiv:2509. 15210v2 Announce Type: replace-cross Abstract: Realistic sound simulation plays a critical role in many applications.
PathRIR: Physics-Guided Acoustic Path Selection and Late-Tail Compensation for Fast Room Impulse Response Simulation
arXiv:2607. 23293v1 Announce Type: cross Abstract: Image-source-method (ISM)-based room impulse response (RIR) simulation is a useful and physically interpretable tool for acoustic scene modeling, but full-order ISM becomes computationally expensive as the reflection order and room complexity increase.
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arXiv AI
Jul 2
Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation
arXiv:2605. 07694v2 Announce Type: replace-cross Abstract: Single-channel speaker distance estimation has recently achieved centimeter-level accuracy in simulated environments, yet it remains unclear which components of the room impulse response (RIR) the model exploits and how performance depends on the recording conditions.
arXiv AI
Jul 1
Improving multichannel speech enhancement through accurate room-acoustic simulations
arXiv:2606. 31552v1 Announce Type: cross Abstract: Room-acoustic simulations are widely used to augment training data for deep-learning-based speech enhancement.
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.
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.
arXiv AI
Jul 3
An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation
arXiv:2607. 02119v1 Announce Type: cross Abstract: While Large Multimodal Models excel in comprehension, high-throughput inference engines lack native support for multimodal generation.
arXiv AI
Aug 5
StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement
arXiv:2603. 09234v2 Announce Type: cross Abstract: Achieving high perceptual quality without hallucination remains a challenge in generative speech enhancement (SE).
arXiv AI
Aug 5
GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement
arXiv:2604. 01832v1 Announce Type: cross Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge.
arXiv Machine Learning
Jun 16
SSNAPS: Audio-Visual Separation of Speech and Background Noise with Diffusion Inverse Sampling
arXiv:2602. 01394v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise.
arXiv AI
Jul 3
SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios
arXiv:2607. 02343v1 Announce Type: cross Abstract: Humans can selectively attend to a target sound and estimate its direction in complex scenarios, whereas such selective localization remains challenging for current deep learning-based systems.
arXiv AI
Jun 30
How to Leverage Synthetic Speech for LLM-Based ASR Systems?
arXiv:2606. 29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic speech recognition (ASR) without exposing sensitive customer recordings.
arXiv AI
Jul 16
A Hybrid Mamba for Audio-Visual Navigation
arXiv:2607. 13110v1 Announce Type: cross Abstract: Since the paradigm centered on convolutional neural networks and recurrent architectures was established in 2020, the fundamental backbone networks for audio-visual navigation have undergone no essential changes for more than five years, making them inadequate to support efficient representation of dynamic multimodal sequences.