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arXiv · 2609.37691

Signal-Independent and Signal-Dependent Neural Ambisonic Matrix Encoding for Arbitrary Arrays with Variable Microphone Counts

Shichao Hu·Zhiheng Jin·Chunyang Xu·Mengyao Zhu
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Abstract

Recent neural Ambisonic encoders accommodate diverse array geometries, yet many existing neural encoders require a fixed microphone count because the number of microphone channels is embedded in the network architecture. This requirement limits deployment across devices with different microphone configurations and adaptation to changes in available channels. To address this limitation, we investigate Transformer-based matrix encoding for arbitrary microphone arrays with variable microphone counts. This is achieved through shared microphone-wise processing and masked self-attention that models inter-microphone relationships across variable-size arrays. Within this framework, we consider signal-independent (SI) encoding, which predicts encoding matrices from array transfer functions, and introduce a signal-dependent (SD) extension that additionally incorporates the observed microphone signals. Both models are trained on simulated scenes using LibriSpeech sources and extensively evaluated under changes in source type, unseen microphone counts, and increased source counts beyond those used during training. Both SI and SD outperform conventional least-squares (LS) encoding in aggregate reconstruction performance across the evaluated conditions. SD consistently achieves stronger overall performance than SI. These results demonstrate that the proposed framework enables array-agnostic Ambisonic encoding while retaining generalization across microphone counts and acoustic source conditions.

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Shichao Hu, Zhiheng Jin, Chunyang Xu, Mengyao Zhu. 2026-09-29. Signal-Independent and Signal-Dependent Neural Ambisonic Matrix Encoding for Arbitrary Arrays with Variable Microphone Counts. https://arxiv.org/abs/2609.37691

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