Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2510.22258

Binaural Signal Matching with Wearable Arrays for Near-Field Sources and Directional Focus

Sapir Goldring·Zamir Ben Hur·David Lou Alon·Chad McKell·Sebastian Prepelita·Boaz Rafaely
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper investigates the performance of Binaural Signal Matching (BSM) methods for near-field sound reproduction using a wearable glasses-mounted microphone array. BSM is a flexible, signal-independent approach for binaural rendering with arbitrary arrays, but its conventional formulation assumes far-field sources. In our previous work, we proposed a near-field extension of BSM (NF-BSM) that incorporates distance-dependent modeling and showed improved performance over far-field BSM using analytic data, though degradation persisted for sources very close to the array. In this study, we extend that analysis by using realistic simulated data of near-field Head-Related Transfer Functions (HRTFs) and Acoustic Transfer Functions (ATFs) of the array, accounting for listener head rotation and evaluating binaural cues such as interaural level and time differences (ILD and ITD). A key contribution is the introduction of a Field of View (FoV) weighting, designed to emphasize perceptually relevant directions and improve robustness under challenging conditions. Results from both simulation and a listening test confirm that NF-BSM outperforms traditional far-field BSM in near-field scenarios, and that the proposed NF-FoV-BSM method achieves the best perceptual and objective quality among all tested methods, particularly at close source distances and under head rotation. These findings highlight the limitations for far-field models in near-field sources and demonstrate that incorporating source distance and directional weighting can significantly improve binaural reproduction performance for wearable spatial audio systems.

Explore related subjects

eess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sapir Goldring, Zamir Ben Hur, David Lou Alon, Chad McKell, Sebastian Prepelita, Boaz Rafaely. 2025-10-25. Binaural Signal Matching with Wearable Arrays for Near-Field Sources and Directional Focus. https://arxiv.org/abs/2510.22258

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Diarization Error Decomposition Under Pause Annotation Ambiguity

Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morphological closing, which has been used for pause-tolerant diarization evaluation, discards segment-level distinctions. Instead, we propose an exact, overlap-aware decomposition of standard DER into a pause-attributable component, consisting of errors compatible with pause filling, and a residual core component that can serve as a proxy for intrinsic diarization errors. The decomposition leaves DER unchanged, while the pause-attributable and core components vary monotonically with the pause threshold and eventually saturate. Experiments spanning synthetic transformations, annotation mismatch, cross-domain evaluation, and tight-boundary diarization show that the decomposition reveals error sources not apparent from standard DER.

eess.AS↗
arXiv2026

Less can be More: What Aspects of Speech Drive End-of-Turn Detection

In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.

eess.AS↗
arXiv2026

Downstream-Task-Aware Unified Source Separation

Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account for downstream task requirements, such as whether the enhanced speech will be used for human listening or automatic speech recognition (ASR). In this paper, we propose a prompt extension framework for TUSS that incorporates downstream task information into the input prompts and switches the loss function according to the given prompt during training, enabling outputs with different signal characteristics at inference time. Specifically, we introduce an ASR-dedicated prompt paired with a regularized loss function that reduces speech artifacts to improve ASR robustness, while the standard prompt is paired with the conventional SNR loss function. Experiments on the LibriSpeech and JNAS corpora demonstrate that the proposed joint-training scheme enables a single model to improve ASR performance over noisy input across a wide range of SNR conditions by selecting the ASR-dedicated prompt, while maintaining general speech enhancement quality when the standard prompt is used.

eess.AS↗