Three-Channel Audio Codec

• Three-Channel Codec is a multi-channel audio compression system that encodes three interrelated audio streams while preserving spatial cues such as phase differences and amplitude ratios.
• It combines traditional transform-based methods with neural architectures to achieve efficient bitrates (e.g., 12–32 kbps) while ensuring compatibility with stereo and mono playback.
• Practical designs emphasize optimal bit allocation, decorrelation via DFT, and performance metrics like SI-SDR and MUSHRA to maintain high-quality spatial audio reproduction.

A three-channel codec is a specialized multi-channel audio compression system designed to encode and decode audio signals from a three-channel source, such as a microphone array or a multi-track audio stream. The goals of a three-channel codec typically include minimizing bitrate for a fixed target (e.g., 12–32 kbps total), preserving inter-channel phase and amplitude relationships critical for spatial audio reproduction or beamforming, and ensuring backward or cross-mode compatibility (e.g., with stereo or mono playback). Recent progress spans traditional transform-based approaches (e.g., DFT+Opus) and neural architectures (e.g., SpatialCodec, VCNAC), reflecting milestones in far-field ASR, spatial audio, and surround sound applications.

1. Fundamental Principles and Design Objectives

Three-channel codecs are distinguished by the need to simultaneously compress spatially related audio streams while retaining sufficient spatial cues (notably inter-channel phase differences and magnitude ratios) to enable downstream signal processing tasks, such as beamforming or localization. The design targets include:

• Bit budget minimization under strict constraints (e.g., $B_\text{tot} \in \{16,24,32\}$ kbps)
• Spatial cue preservation (for array processing or immersive playback)
• Geometry-independence with respect to microphone arrangement
• Backward compatibility and flexibility for rendering as stereo or mono

Approaches vary from parametric transform coding to deep neural network-based schemes, but all require care to avoid artifacts (loss of phase, decorrelation, or spatial colorations) that degrade intelligibility, spatial accuracy, or listening quality (Drude et al., 2021, Xu et al., 2023, Grötschla et al., 21 Jan 2026).

2. Transform-Based Three-Channel Codec Architectures

A canonical traditional approach is exemplified by the modified three-channel Opus-based codec (Drude et al., 2021). The signal flow comprises:

1. Channel-wise decorrelation: A $3$-point DFT is applied across channels, yielding one real DC stream ($Y_0$), one real and one imaginary (“side”) stream ($Y_1$, $Y_2$), capturing the relevant spatial subspace.
2. Quantization: Each $Y_m$ is subject to uniform scalar quantization, with step size $\Delta_m$ set via classical rate–distortion theory ($D_m \approx \Delta_m^2/12$, $R_m \approx -\frac{1}{2}\log_2 D_m + C_m$).
3. Joint entropy coding: The quantized streams are packed into a single Opus CELT-mode stream; “pairwise” mid/side coupling compresses residual correlation in the AC pair $\{\mathrm{Re}\,Y_1, \mathrm{Im}\,Y_1\}$.
4. Bit allocation: Bit budgeting is performed either uniformly or power-proportionally ($$B_m = B_\text{tot} \cdot \frac{\sigma_m}{\sum_n \sigma_n}$$), though in practice a uniform split suffices for $B_\text{tot} \gtrsim 20$ kbps.
5. Bitstream structure: Each 20 ms frame contains headers (sync, codec flags, allocation fields) and payload. The decoder applies the inverse DFT per frame to reconstruct time-aligned channels.

Notably, this construction is nearly lossless spatially in the high-bitrate regime and robust to microphone geometry. The 3×3 DFT matrix is unitary (with $1/\sqrt{3}$ scaling), ensuring no energy loss during transform, and approximates the Karhunen–Loève transform for arrays with Toeplitz spatial covariance (Drude et al., 2021).

3. Neural Three-Channel Codecs: Architectures and Losses

Contemporary neural approaches include the “SpatialCodec” (Xu et al., 2023) and VCNAC (Grötschla et al., 21 Jan 2026) frameworks, each reflecting a distinct modeling philosophy:

SpatialCodec (for microphone arrays and spatial speech coding):

• Reference-branch sub-band codec applies a deep, 2D convolutional network (six layers + residual units) to the STFT of the chosen reference channel, compressing its spectral-temporal evolution using two-layer residual vector quantization (RVQ); typical bitrate: 6 kbps for the reference.
• Spatial branch conditions on both the reference STFT and the inter-channel spatial covariance $\Phi(t,f) = X(t,f)X(t,f)^\mathrm{H}$, processed via deep 2D CNN layers. The decoder predicts two complex ratio filters (CRF) applied as FIR spatio-spectral filters to reconstruct the two non-reference STFTs.
• Compound loss combines multi-resolution STFT losses, adversarial and codebook commitment penalties, and time-domain SNR for both the reference and CRF-based branches.
• The system outputs two quantized bitstreams ($C_\text{ref}$ and $C_\text{spat}$), decoded to three output waveforms via ISTFT. The entire pipeline uses ∼8M parameters, with a total bitrate of 12 kbps (Xu et al., 2023).

VCNAC (Variable-Channel Neural Audio Codec):

• Parallel encoder streams: $C$ streams (here $C=3$) use shared 1D convolutional weights, each ingesting a channel. Channel identity is preserved via learnable embeddings.
• Fusion and vector quantization: Channel streams are summed at the bottleneck, quantized using global RVQ (26 codebooks; total $\sim$7.85 kbps at 25 frames/sec), and split back for decoding with a mirrored structure.
• Inter-channel attention: Lightweight transformer layers enable crosstalk modeling pre-fusion and post-splitting.
• Channel compatibility objectives are enforced via stereo and mono downmix losses on the reconstructed signals, in addition to multi-scale mel-spectrogram loss and adversarial (GAN) losses.
• Bitrate is invariant to channel count (for fixed codebooks), enabling scaling to 3- or 5.1-channel audio without architecture changes. At $C=3$ default, per-channel bitrate is $2.62$ kbps (Grötschla et al., 21 Jan 2026).

4. Quantitative Performance and Benchmarks

Objective metrics and task-specific benchmarks illustrate the trade-offs between coding efficiency, spatial fidelity, and end-task performance.

Codec & Setting	Bitrate (kbps)	Main Metrics	Notable Results
3-ch Opus DFT+pair (Drude et al., 2021)	16/24/32	nWER, WER, ASR	10–12% lower WER than per-ch Opus; within 1–2% of uncompressed at 32kbps
SpatialCodec (Xu et al., 2023)	12	SS, DoA, RTF, Beamformed	SS≈0.95, DoA err≈14°, RTF err≈0.73, PESQ≈2.92, STOI≈0.82, SNR≈8.3dB
VCNAC (Grötschla et al., 21 Jan 2026) (C=3)	7.85	SI-SDR, MUSHRA, ΔMel	SI-SDR front channels ≈5–6 dB, center ≈0–2 dB, MUSHRA >80/100

• For far-field ASR with joint channel coding, the three-channel DFT+Opus approach achieves up to 20% bitrate reduction at the cost of only 2–5% WER loss compared to uncompressed signals.
• SpatialCodec at only 12 kbps total outperforms high-bitrate Opus and black-box neural MIMO codecs on spatial similarity (SS≈0.95) and achieves lower direction-of-arrival (DoA) and relative transfer function (RTF) errors.
• VCNAC maintains high-perceptual quality (>80/100 on MUSHRA) even for total bitrates <8 kbps, due to its fusion-quantization and cross-channel attention design. Per-channel SI-SDR for L/R approaches stereo baseline, with the center channel rendered accurately through shared-latent representations.

5. Spatial Cue Preservation and Evaluation Metrics

Preservation of spatial cues is a primary design driver and is objectively evaluated using several metrics:

• Relative Transfer Function (RTF) error quantifies angular fidelity in principal transfer vectors.
• MUSIC DoA error uses array processing (e.g., classic MUSIC) to estimate the error in direction-of-arrival localization on reconstructed signals.
• Spatial Similarity (SS) computes beamspace cosine similarity between original and reconstructed spatial features over a B-dimensional (e.g., $B=50$) super-directive beamformer bank.
• Beamformed audio quality is assessed both intrusively and non-intrusively via SNR, PESQ, STOI, and DNSMOS metrics, using the true direct-path DoA for beamforming.
• Perceptual listening (MUSHRA) and submix losses ensure that codecs are robust to downmixing and suitable for stereo or mono playback without spatial artifacts (Xu et al., 2023, Grötschla et al., 21 Jan 2026).

6. Implementation Practices and Integration

Best practices for deploying three-channel codecs include:

• Frame sizing and overlap: 20 ms frames with 25% overlap (as in Opus); or 50 frames/sec for neural models.
• Decorrelation transform: Use of a DFT across channels to maximize orthogonality, geometric invariance, and redundancy reduction.
• Bit allocation: Power-proportional at low bitrates; uniform split becomes efficient for $B_\text{tot} \gtrsim 20$ kbps (Drude et al., 2021).
• Flag configuration: In DFT+Opus, waveform-matching flags ON, intensity stereo and spectral folding minimized, pairwise coupling applied to real/imag AC pair to maximize bit efficiency.
• Neural codec scaling: For VCNAC, addition of a third channel simply instantiates an additional encoder/decoder stream and embedding, without modifying the shared RVQ or convnet weight structure.
• Interfacing with signal processing pipelines: Opus-based systems are readily inserted into existing beamforming and ASR workflows, while neural codecs such as SpatialCodec can directly preserve statistics critical for downstream spatial inference (Drude et al., 2021, Xu et al., 2023, Grötschla et al., 21 Jan 2026).

7. Applications and Forward Directions

Three-channel codecs are integral to:

• Far-field automatic speech recognition (ASR): Efficient multi-mic signal transmission for cloud-based inference (Drude et al., 2021).
• Spatial and surround audio rendering: Immersive playback in music and cinematic content, with downmix compatibility (Grötschla et al., 21 Jan 2026).
• MIMO neural speech processing: Neural systems that transfer spatial knowledge compactly and enable flexible rendering (SpatialCodec branch) (Xu et al., 2023).
• Research in robust spatial audio: The trend toward universal, codebook-sharing neural codecs (VCNAC) enables scalable, cross-configuration training and inference.

As neural and traditional systems converge in deployment efficiency and audio quality, ongoing work seeks to further lower bitrates while generalizing to larger arrays and challenging acoustic conditions. The preservation and accurate reconstruction of spatial cues remains a central, quantifiable challenge, addressed via increasingly sophisticated architectures, loss designs, and spatial feature analyses (Drude et al., 2021, Xu et al., 2023, Grötschla et al., 21 Jan 2026).