An Efficient Parametric Codec for Low-Bitrate First-Order Ambisonics

Wei-Ting Lai; Amy Bastine; Lachlan Birnie; Thushara D. Abhayapala; Prasanga N. Samarasinghe

eess.ASSep 27, 2026

An Efficient Parametric Codec for Low-Bitrate First-Order Ambisonics

Authors: Wei-Ting Lai, Amy Bastine, Lachlan Birnie, Thushara D. Abhayapala, Prasanga N. Samarasinghe

Organizations: Audio & Acoustic Signal Processing Group, The Australian National University, Canberra, Australia

Abstract

Driven by the rapid growth of immersive teleconferencing and generative spatial audio, efficient low-bitrate coding of first-order Ambisonics (FOA) has become increasingly important. In this work, we develop a lightweight parametric FOA codec that retains the standard Directional Audio Coding analysis and synthesis while redesigning the spatial metadata quantization scheme. Rather than quantizing direction-of-arrival (DOA) and diffuseness independently, we combine them into a 3-D directivity vector and jointly quantize these vectors across frequency bands via residual vector quantization (RVQ). The RVQ codebooks are optimized within minutes via stage-wise k-means without backpropagation, yielding a constant-bitrate (CBR) representation that decouples metadata rate from the number of frequency bands. Evaluations show that our approach outperforms low-bitrate perceptual codecs in FOA reconstruction, remains competitive with neural codecs on the downstream Sound Event Localization and Detection task, and maintains robust performance when paired with different external monaural codecs. Given its lightweight training, strong performance, and CBR design, we consider the proposed method to be a favorable and reproducible baseline for future FOA codec research.

Figures & tables

Fig. 1 : Overview of the proposed parametric FOA codec.

Method	W-ch	$\mathbf{R_{\rm sp}}$	$\mathbf{S}$	$\mathbf{R_{\rm tot}}$	SpatialVCTK					STARSS23				MEIR
Method	W-ch	$\mathbf{R_{\rm sp}}$	$\mathbf{S}$	$\mathbf{R_{\rm tot}}$	STFT	Mel	Ang	Diff	WER	STFT	Mel	Ang	Diff	STFT	Mel	Ang	Diff
MPEG-H [ 14 ]	–	–	–	$\approx$ 48	1.31	0.82	2.11 ∘	0.02	0.20	1.45	0.81	10.37 ∘	0.05	2.39	1.47	27.53 ∘	0.16
FOA-VQGAN [ 35 ]	–	–	–	0.9	1.62	1.39	3.96 ∘	–	0.67	–	–	–	–	1.82	1.43	25.83 ∘	–
Opus [ 36 ]	–	–	–	$\approx$ 24	2.42	1.70	10.04 ∘	0.11	0.23	2.50	1.73	28.46 ∘	0.12	4.16	2.48	56.53 ∘	0.27
Opus [ 36 ]	–	–	–	$\approx$ 32	2.22	1.44	5.31 ∘	0.05	0.19	2.35	1.52	19.18 ∘	0.08	4.02	2.27	38.67 ∘	0.24
DirAC [ 10 ]	EnCodec [ 7 ]	0.75	–	6.75	2.06	2.21	29.21 ∘	0.00	0.15	2.04	1.67	47.42 ∘	0.30	2.24	1.45	31.22 ∘	0.24

Table 1 : FOA reconstruction performance on SpatialVCTK, STARSS23, and MEIR. Lower is better for all metrics.

Fig. 2 : Reconstruction of the 3-D directivity vector for a STARSS23 clip ( $R_{\mathrm{sp}}=750$ bps for both methods). For visualization, we show the $y$ -axis component $[\mathbf{v}_{t,b}]_{y}$ . The rightmost curve reports the energy-weighted distortion $\mathcal{D}=\sum_{t}e_{t}d_{t}/\sum_{t}e_{t}$ .

Method	W-ch	F-score $\uparrow$	LE $\downarrow$
DCASE baseline [ 1 ]	–	29.9	$22^{\circ}$
FOA-VQGAN [ 35 ]	–	11.1	$37^{\circ}$
Proposed	EnCodec	14.2	$\mathbf{30^{\circ}}$
Proposed	DAC	13.8	$34^{\circ}$

Table 2 : STARSS23 SELD results. Results from the uncompressed reference are underlined. Best results among codecs are in bold.

Explore similar work

Jun 3, 2026cs.SD

Flow-HOA: Generative Joint Optimization for Ambisonics Encoding via Flow Matching

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.

Yuhuan You, Yufan Qian, Tianshu Qu +2

State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, China · 2Beijing Xiaomi Mobile Software Co., Ltd · 3Xiaomi Communications Co., Ltd

Spatial Audio Microphone Array Impulse Response

Ask PDF

Jun 16, 2026eess.AS

Generalised Transcoding Framework for Arbitrary Spatial Audio Capture and Playback Formats

This article introduces a unified framework for the parametric analysis and reproduction of spatial sound scenes captured either as Ambisonic signals or as raw microphone array signals. The proposed method estimates time-frequency-dependent spatial metadata that characterises a variable number of primary source components and an ambience component with its own angular power distribution, whose parameters fit the observed spatial covariances of the captured signals. This metadata is used to construct spatial covariances of the target playback formats, which are then used to derive optimal mixing matrices for transcoding the scene for playback over the target reproduction system. The method additionally handles independent rotations of both capture and playback setups. Real-time implementations of the method and other existing state-of-the-art parametric renderers are compared in a listening test using simulated scenes from Ambisonic, spherical, and head-worn arrays. The results highlight perceptual benefits of the proposed framework across a diverse range of content and receiver configurations, particularly for lower-order and geometrically constrained microphone arrays.

Archontis Politis, Janani Fernandez, Leo McCormack

Faculty of Information Technology and Communication Sciences, Tampere University, Finland · Department of Information and Communications Engineering, Aalto University, Espoo, Finland

Spatial Audio Microphone Array Opencode Covariance

Ask PDF

Sep 3, 2026cs.SD

PACodec: A Low-bitrate Neural Speech Codec with Parallel Additive Vector Quantization

This paper proposes PACodec, a novel low-bitrate neural speech codec based on parallel additive vector quantization (PAVQ). Unlike the mainstream residual vector quantization (RVQ) used in most neural speech codecs, where vector quantizers (VQs) are sequentially dependent, the PAVQ strategy adopted in PACodec aggregates parallel quantization results to optimize bitrate usage. Specifically, the PAVQ adopts a "global-local-global" (GLG) design: the global encoded features are quantized in parallel by multiple independent VQs, each attending to a local component of the representation, and their outputs are aggregated through addition to yield the final global quantization result for decoding. Experimental results show that PACodec, as each VQ focuses only on local information, supports smaller codebooks and reduces bitrate by 30% compared with baselines at the same decoding quality, with only minor model complexity. Further analysis shows that, owing to the GLG framework of PAVQ, the proposed PACodec is disentanglement-friendly, and each independent VQ captures different aspects of speech, e.g., content, timbre, and acoustic details, suggesting potential for application to downstream tasks such as voice conversion.

Fei Liu, Yang Ai, Xiao-Hang Jiang +1

National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China, Hefei, China

Neural Speech Codecs Residual Vector Quantization Quantizer

Ask PDF