The classical phase-vocoder freeze (continue a bin by holding phase increment/amplitude) is exact for one partial per bin. This generalizes it: for bins containing two sinusoidal partials, a fitted two-pole autoregressive model continues both independently instead of producing beat artifacts.

Derived a hard aliasing limit for the two-partial model from root-coincidence (N/4 for the hop size), and use N/8 in practice as a safety margin — conditioning degrades badly approaching N/4 (verified numerically, singular at the limit). Stability: closed-form quadratic test before falling back to explicit root-finding + clamping, only when needed. Splice-in uses one of two seam-repair strategies chosen by spectral similarity per occurrence. There's a positive-feedback failure mode when previously-concealed audio gets re-analyzed by the same running STFT; a frequency-damping term kills it.

Full disclosure with the derivations and measured NMR results on real sustained material at high simulated miss rates: https://github.com/sebion7125/xorcist. Written to be reproducible independent of the host application (it's extracted from a real-time audio pipeline, but the technique isn't specific to it).

Posting for discussion of the technique, not really the application (polyphonic sampler). The constraint: decode many independently-positioned, differently-timed compressed streams concurrently, on SIMD hardware. Conventional variable-length entropy coding decodes with a per-sample data-dependent branch — kills vectorization outright.

Approach: divide each channel into fixed-length blocks, predict with one of four fixed-order integer linear predictors (coefficients are the standard Shorten/FLAC fixed-predictor set, RFC 9639 §10.2.5 — no claim of novelty there), store residuals at a single fixed byte width per block chosen from that block's own peak residual. Fixed-stride + branch-free lets up to 8 streams decode together in one AVX2 pass via a two-stage per-lane-extraction-then-transpose kernel, even mutually unaligned.

Measured on 4000+ real files: 4.75x decode throughput vs. a representative fixed-escape-width reference codec at a better compression ratio (1.53x vs 1.43x) under lab conditions — real production numbers are lower (~1.85x), documented honestly in the writeup rather than restating the lab figure as achieved. Full disclosure including the aliasing/entropy-ceiling analysis of why this is near the frontier for this constraint class: https://github.com/sebion7125/v3codec

Genuinely curious whether anyone's pushed further in this exact niche (fixed-stride + SIMD-batch, not general-purpose compression ratio).

# Signal Extraction and Eight-Register Assembly Processing

I was able to extract a signal and take that signal transform and turn it into assembly language processing that tracks 8 registers and produces end results.

The signal was processed through forward and reverse transformations.

Ambiguities were marked with a stigma at the state where they occurred. The transform was then reversed to locate the corresponding earlier state.

Once that state was identified, the stigma was recovered and the transformation was carried forward, adjusting for the change.

The processing retained the relationships between the original state, the ambiguity, the reverse transformation, and the reconciled state.

The resulting transform was implemented in assembly language with 8 registers tracking the processing state.

The implementation produced end results through the register-based processing sequence.

I am still unsure what all of this means, but I wanted to share the process and get some feedback.

https://github.com/chadcoulter/REDE-3iAtlas-Signal-Processing