BSM-iMagLS: ILD Informed Binaural Signal Matching for Reproduction with Head-Mounted Microphone Arrays
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915357911941120 |
|---|---|
| author | Berebi, Or Ben-Hur, Zamir Alon, David Lou Rafaely, Boaz |
| author_facet | Berebi, Or Ben-Hur, Zamir Alon, David Lou Rafaely, Boaz |
| contents | 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 reproduction a critical component. As capture devices, wearable arrays with only a few microphones with irregular arrangement face challenges in achieving a reproduction quality comparable to that of arrays with a large number of microphones. Binaural signal matching (BSM) has recently been presented as a signal-independent approach for generating high-quality binaural signal using only a few microphones, which is further improved using magnitude-least squares (MagLS) optimization at high frequencies. This paper extends BSM with MagLS by introducing interaural level difference (ILD) into the MagLS, integrated into BSM (BSM-iMagLS). Using a deep neural network (DNN)-based solver, BSM-iMagLS achieves joint optimization of magnitude, ILD, and magnitude derivatives, improving spatial fidelity. Performance is validated through theoretical analysis, numerical simulations with diverse HRTFs and head-mounted array geometries, and listening experiments, demonstrating a substantial reduction in ILD errors while maintaining comparable magnitude accuracy to state-of-the-art solutions. The results highlight the potential of BSM-iMagLS to enhance binaural reproduction for wearable and portable devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BSM-iMagLS: ILD Informed Binaural Signal Matching for Reproduction with Head-Mounted Microphone Arrays Berebi, Or Ben-Hur, Zamir Alon, David Lou Rafaely, Boaz Audio and Speech Processing Sound 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 reproduction a critical component. As capture devices, wearable arrays with only a few microphones with irregular arrangement face challenges in achieving a reproduction quality comparable to that of arrays with a large number of microphones. Binaural signal matching (BSM) has recently been presented as a signal-independent approach for generating high-quality binaural signal using only a few microphones, which is further improved using magnitude-least squares (MagLS) optimization at high frequencies. This paper extends BSM with MagLS by introducing interaural level difference (ILD) into the MagLS, integrated into BSM (BSM-iMagLS). Using a deep neural network (DNN)-based solver, BSM-iMagLS achieves joint optimization of magnitude, ILD, and magnitude derivatives, improving spatial fidelity. Performance is validated through theoretical analysis, numerical simulations with diverse HRTFs and head-mounted array geometries, and listening experiments, demonstrating a substantial reduction in ILD errors while maintaining comparable magnitude accuracy to state-of-the-art solutions. The results highlight the potential of BSM-iMagLS to enhance binaural reproduction for wearable and portable devices. |
| title | BSM-iMagLS: ILD Informed Binaural Signal Matching for Reproduction with Head-Mounted Microphone Arrays |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2501.18227 |