31 citations · 243 across the 42 of their papers we have counts for
13 papers · 1 filter
SpatialNet with Binaural Loss Function for Correcting Binaural Signal Matching Outputs under Head Rotations
Dor Shamay, Boaz Rafaely
Binaural reproduction is gaining increasing attention with the rise of devices such as virtual reality headsets, smart glasses, and head-tracked headphones. Achieving accurate bina…
SRP-PHAT-NET: A Reliability-Driven DNN for Reverberant Speaker Localization
Bar Shaybet, Vladimir Tourbabin, Boaz Rafaely
Accurate Direction-of-Arrival (DOA) estimation in reverberant environments remains a fundamental challenge for spatial audio applications. While deep learning methods have shown st…
HyBeam: Hybrid Microphone-Beamforming Array-Agnostic Speech Enhancement for Wearables
Yuval Bar Ilan, Boaz Rafaely, Vladimir Tourbabin
Speech enhancement is a fundamental challenge in signal processing, particularly when robustness is required across diverse acoustic conditions and microphone setups. Deep learning…
Binaural Signal Matching with Wearable Arrays for Near-Field Sources and Directional Focus
Sapir Goldring, Zamir Ben Hur, David Lou Alon +3
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 flex…
An Efficient Neural Network for Modeling Human Auditory Neurograms for Speech
Eylon Zohar, Israel Nelken, Boaz Rafaely
Classical auditory-periphery models, exemplified by Bruce et al., 2018, provide high-fidelity simulations but are stochastic and computationally demanding, limiting large-scale exp…
DiffAU: Diffusion-Based Ambisonics Upscaling
Amit Milstein, Nir Shlezinger, Boaz Rafaely
Spatial audio enhances immersion by reproducing 3D sound fields, with Ambisonics offering a scalable format for this purpose. While first-order Ambisonics (FOA) notably facilitates…
AmbiDrop: Array-Agnostic Speech Enhancement Using Ambisonics Encoding and Dropout-Based Learning
Michael Tatarjitzky, Boaz Rafaely
Multichannel speech enhancement leverages spatial cues to improve intelligibility and quality, but most learning-based methods rely on specific microphone array geometry, unable to…
Binaural Signal Matching with Wearable Arrays for Near-Field Sources
Sapir Goldring, Zamir Ben Hur, David Lou Alon +1
Binaural reproduction methods aim to recreate an acoustic scene for a listener over headphones, offering immersive experiences in applications such as Virtual Reality (VR) and tele…
Ambisonics Encoder for Wearable Array with Improved Binaural Reproduction
Yhonatan Gayer, Vladimir Tourbabin, Zamir Ben-Hur +2
Ambisonics Signal Matching (ASM) is a recently proposed signal-independent approach to encoding Ambisonic signal from wearable microphone arrays, enabling efficient and standardize…
Array-Aware Ambisonics and HRTF Encoding for Binaural Reproduction With Wearable Arrays
Yhonatan Gayer, Vladimir Tourbabin, Zamir Ben Hur +2
This work introduces a novel method for binaural reproduction from arbitrary microphone arrays, based on array-aware optimization of Ambisonics encoding through Head-Related Transf…
Loss functions incorporating auditory spatial perception in deep learning -- a review
Boaz Rafaely, Stefan Weinzierl, Or Berebi +1
Binaural reproduction aims to deliver immersive spatial audio with high perceptual realism over headphones. Loss functions play a central role in optimizing and evaluating algorith…
Ambisonics Binaural Rendering via Masked Magnitude Least Squares
Or Berebi, Fabian Brinkmann, Stefan Weinzierl +1
Ambisonics rendering has become an integral part of 3D audio for headphones. It works well with existing recording hardware, the processing cost is mostly independent of the number…
BSM-iMagLS: ILD Informed Binaural Signal Matching for Reproduction with Head-Mounted Microphone Arrays
Or Berebi, Zamir Ben-Hur, David Lou Alon +1
Headphone listening in applications such as augmented and virtual reality (AR and VR) relies on high-quality spatial audio to ensure immersion, making accurate binaural reproductio…