{"ID":3052373,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T07:53:07.675991959Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04570","arxiv_id":"2606.04570","title":"Flow-HOA: Generative Joint Optimization for Ambisonics Encoding via Flow Matching","abstract":"Higher-Order Ambisonics (HOA) encoding from sparse, irregular microphone arrays remains a critical challenge for consumer spatial audio capture in immersive communication and XR. We propose Flow-HOA, a generative framework that jointly optimizes a multi-dimensional objective encompassing time-domain, spectral, and spatial fidelity while producing a deployable, time-invariant bank of Finite Impulse Response (FIR) encoding filters. Using conditional flow matching, the model learns to map a simple prior distribution to the target distribution of FIR filter coefficients. Training is guided by a composite loss that balances time-domain waveform fidelity, multi-resolution spectral consistency, sub-band energy preservation, and spatial directivity constraints. Objective evaluations on synthetically simulated data demonstrate improved performance over strong model-based baselines in both signal fidelity and spatial accuracy metrics. Subjective listening tests on real microphone array recordings further confirm that Flow-HOA yields higher overall sound quality with reduced artifacts, demonstrating generalization from synthetic training data to real-world capture conditions.","short_abstract":"Higher-Order Ambisonics (HOA) encoding from sparse, irregular microphone arrays remains a critical challenge for consumer spatial audio capture in immersive communication and XR. We propose Flow-HOA, a generative framework that jointly optimizes a multi-dimensional objective encompassing time-domain, spectral, and spat...","url_abs":"https://arxiv.org/abs/2606.04570","url_pdf":"https://arxiv.org/pdf/2606.04570v1","authors":"[\"Yuhuan You\",\"Yufan Qian\",\"Tianshu Qu\",\"Bin Wang\",\"Xueyang Lv\"]","published":"2026-06-03T08:03:51Z","proceeding":"cs.SD","tasks":"[\"cs.SD\"]","methods":"[]","has_code":false}