Ambisonics Encoding For Arbitrary Microphone Arrays Incorporating Residual Channels For Binaural Reproduction
In the rapidly evolving fields of virtual and augmented reality, accurate spatial audio capture and reproduction are essential. For these applications, Ambisonics has emerged as a standard format. However, existing methods for encoding Ambisonics signals from arbitrary microphone arrays face challenges, such as errors due to the irregular array configurations and limited spatial resolution resulting from a typically small number of microphones. To address these limitations and challenges, a mathematical framework for studying Ambisonics encoding is presented, highlighting the importance of incorporating the full steering function, and providing a novel measure for predicting the accuracy of encoding each Ambisonics channel from the steering functions alone. Furthermore, novel residual channels are formulated supplementing the Ambisonics channels. A simulation study for several array configurations demonstrates a reduction in binaural error for this approach.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Neural Ambisonics encoding for compact irregular microphone arrays
Ambisonics encoding of microphone array signals can enable various spatial audio applications, such as virtual reality or telepresence, but it is typically designed for uniformly-spaced spherical microphone arrays. This …
Gen-A: Generalizing Ambisonics Neural Encoding to Unseen Microphone Arrays
Using deep neural networks (DNNs) for encoding of microphone array (MA) signals to the Ambisonics spatial audio format can surpass certain limitations of established conventional methods, but existing DNN-based methods n…
Neural Ambisonic Encoding For Multi-Speaker Scenarios Using A Circular Microphone Array
Spatial audio formats like Ambisonics are playback device layout-agnostic and well-suited for applications such as teleconferencing and virtual reality. Conventional Ambisonic encoding methods often rely on spherical mic…
Ambisonic Encoding of Signals From Equatorial Microphone Arrays
The equatorial microphone array presented in (Ahrens et al., 2021) computes a spherical harmonic (SH) representation of a sound field based on pressure sensors along the equator of a rigid spherical baffle. The original …
L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing
The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and det…
Audio Signal ProcessingBIG-bench Machine LearningSpeech Enhancement