Stem-Native Codec (SNC)

• Stem-Native Codec is an audio format that stores independent semantic stems and a mastering residual to enable interactive remixing and spatial rendering.
• It exploits lower information entropy in separated stems to achieve significant file size reductions, demonstrating a 38.2% reduction versus FLAC in tests.
• Its architecture supports advanced metadata management for context-aware EQ, user remixing, and real-time adaptive playback without extra storage overhead.

The Stem-Native Codec (SNC) is an audio storage and distribution format designed to address the longstanding trade-off between audio quality, compression efficiency, and functional adaptability in digital music. SNC operates by representing a musical work not as a fused stereo mix but as a multi-stream object: independently encoded semantic stems (vocals, drums, bass, etc.) supplemented by a low-energy residual that captures the difference between the sum of decoded stems and the original mix. Exploiting the lower information entropy characterizing separated stems in comparison to the full mix, SNC achieves significant file size reductions over classic lossless codecs while enabling transformative playback features such as user remixing, spatial rendering, and context-aware adaptation, all without requiring additional storage overhead. SNC is implemented as a Matroska container embedding variable bitrate Opus tracks for each stem, the mastering residual, and associated adaptive metadata (Sufi, 8 Feb 2026).

1. Architecture and Encoding Pipeline

SNC is based on a signal model in which the original mix $M(t)$ is the sum of $N$ source stems $S_i(t)$ in the time domain,

$M(t) = \sum_{i=1}^N S_i(t)\,.$

These stems may be obtained from DAW exports (studio quality) or via AI-based music source separation methods, such as Hybrid Transformer models. Each stem $S_i(t)$ is then treated as an independent Opus variable-bitrate (VBR) stream. To ensure perfect fidelity to the original, SNC further stores a mastering residual signal,

$R(t) = M(t) - \sum_{i=1}^N \hat S_i(t)\,,$

where $\hat S_i(t)$ denotes each stem after Opus decoding. This residual compensates for quantization, separation, and mixing artifacts.

The encoding pipeline consists of the following steps:

1. Normalize $M(t)$ to –16 LUFS.
2. Obtain stems $S_i(t)$.
3. For each stem: encode $S_i(t)$ at a target per-stem bitrate using Opus VBR, and decode to obtain $\tilde S_i(t)$.
4. Form the procedural mix $\tilde M_{\rm proc}(t) = \sum_i \tilde S_i(t)$.
5. Compute $R(t) = M(t) - \tilde M_{\rm proc}(t)$.
6. Encode $R(t)$ at 64 kbps VBR.
7. Package all encoded tracks and metadata into a Matroska (.snc) file.

Bitrate allocation prioritizes perceptual importance, with typical allocations of 128 kbps VBR for vocals, 96 kbps VBR per instrumental stem, and 64 kbps VBR for the residual. Metadata (JSON) occupies roughly 0.5% of the file (Sufi, 8 Feb 2026).

Information-theoretically, the SNC design exploits the substantially reduced instantaneous spectral entropy of separated stems compared to mixed audio. Letting $H(X)$ denote the Shannon entropy of audio $X$ (bits/sample), overall bitrate for transparent coding of the mix is: $R_M \propto \int_0^T H(M(t))\,dt\,,$ whereas for SNC,

$$R_{\text{stems}} \propto \sum_{i=1}^N \int_0^T H(S_i(t))\,dt\,,$$

and in practice,

$$\sum_{i=1}^N H(S_i(t)) < H(M(t)) \implies R_{\text{stems}} < R_{M}\,,$$

by subadditivity of entropy and psychoacoustic masking.

2. Compression Efficiency and Perceptual Fidelity

SNC achieves a significant reduction in storage cost compared to traditional lossless formats. On a test track (2 min 18 s, 48 kHz/16-bit stereo, dense mix), using four Hybrid Transformer-separated stems (vocals, drums, bass, other), the following file sizes were observed:

Codec	File Size (MB)	Relative Reduction vs. FLAC
FLAC	12.55	—
SNC	7.76	38.2%
Opus	(256 kbps)	—
MP3	(320 kbps)	—
AAC	(256 kbps)	—

This 38.2% reduction relative to FLAC is calculated as

$$\frac{12.55 - 7.76}{12.55} \approx 0.382\, (38.2\%).$$

SNC maintains perceptual transparency, demonstrated by three objective metrics:

• Short-Time Objective Intelligibility (STOI): 0.996 (target $>0.95$)
• Spectral Convergence (SC): 0.0402 (target $<0.05$),

$$\mathrm{SC} = \frac{\|\ |X| - |\hat X| \|_F}{\|\ |X|\ \|_F}$$

• Signal-to-Noise Ratio (SNR): 24.86 dB (target $>20$ dB),

$$\mathrm{SNR} = 10\log_{10} \frac{\sum_t M(t)^2}{\sum_t (M(t)-\tilde M(t))^2}$$

All thresholds for high-fidelity transparent coding are met (Sufi, 8 Feb 2026).

3. Advanced Playback, Spatial Audio, and Adaptivity

By virtue of storing stems and residuals independently, SNC enables a rich object-based audio paradigm. This provides:

• Context-aware EQ/compression: Dynamic adjustment of gain or compression per stem, for example boosting vocals in noisy environments.
• User remixing: Enabling muting, soloing, or rebalancing stems conditionally on artist-specified permissions.
• Spatial rendering: Metadata encodes for each stem its 3D location $(x_i, y_i, z_i)$, spread angle $\theta_i$, and reverb parameters, enabling client-side panning and head-related transfer function (HRTF) rendering.

Real-time playback involves per-stem gain $\alpha_i$ and binaural/spatial filtering $h_{i,L/R}$,

$$y_{L}(t) = \sum_{i=1}^N \alpha_i \bigl(h_{i,L} * S_i(t)\bigr) + \bigl(h_{R,\text{residue},L} * R(t)\bigr)$$

and similarly for the right channel.

These features are supported without requiring storage of multiple versions of the track; all adaptive and interactive renderings are generated on the fly at playback (Sufi, 8 Feb 2026).

4. Computational Complexity and Implementation Considerations

The computational cost of SNC is primarily in encoding/decoding $N$ stems and one residual stream through Opus. On current hardware, even mobile SoCs can decode upwards of 10 Opus streams in real-time, implying low practical bottlenecks. The decoding process involves summing of decoded stems, addition of the residual, and final normalization.

Storage savings for SNC are significant compared to FLAC (7.76 MB vs. 12.55 MB in the reported case). The decoding complexity is somewhat higher than a single-track format but remains within the capabilities of contemporary playback devices.

Metadata management is a critical dependency, as adaptive and spatial playback features are contingent on standardized, player-recognized schemas. Stem availability also influences performance: ideal stems from DAW exports minimize residual RMS (observed $-40$ dB), whereas AI-separated stems result in higher energy in the residual (observed $-29.97$ dB RMS) (Sufi, 8 Feb 2026).

5. Comparison with Related Paradigms and Future Work

SNC contrasts with traditional monolithic audio formats by inverting the “mono mix” paradigm: instead of encoding a single channel-mixed waveform, it encodes semantically meaningful components. Existing lossless codecs such as FLAC do not permit interactive features or per-stem manipulation, and lossy codecs (Opus, MP3, AAC) lack both transparency and adaptive remix capability.

Planned and plausible future work in the SNC framework includes:

• Investigation of lossless stem coding (e.g., FLAC per stem) to provide bit-perfect source streams, at the expense of increased file size.
• Perceptual bit allocation across stems informed by psychoacoustic inter-stem masking models.
• Adaptive bitrate allocation using real-time content analysis.
• Implementation of progressive streaming paradigms delivering a base mix first, with subsequent delivery of stems and residuals for enhanced features.
• Formal perceptual listening tests (e.g., ABX) in addition to objective metrics.
• Large-scale deployment studies with streaming services assessing user engagement with interactive and spatial audio content.

6. Summary and Position in Audio Distribution

The Stem-Native Codec is a practical realization of an object-based audio compression and playback paradigm, achieving a 38.2% file-size reduction versus FLAC for the reported case while maintaining perceptual transparency (STOI = 0.996, SC = 0.0402, SNR = 24.86 dB). Its decoupling of compression efficiency from feature richness enables context-aware playback, spatial rendering, and remixing at no extra storage cost, establishing SNC as a path to next-generation, feature-rich audio distribution systems (Sufi, 8 Feb 2026).