JustOneCacophony is an experimental/test implementation of E-AC-3 JOC for studying JOC parsing, reconstruction, rendering, and the associated mathematics.

The project can extract and parse EMDF, ID14 JOC parameters, and ID11 OAMD metadata from common E-AC-3 JOC streams. It combines those data with the core 5.1 PCM decoded by FFmpeg, reconstructs LFE plus 15 object channels, and writes ADM BWF, a WAV file for a selected speaker layout, or direct binaural stereo using a standard SOFA HRTF.

This is research code, not a complete, standards-compliant, or production-grade JOC decoder. It covers only the stream forms currently implemented. Unknown variants fail explicitly—because when the math goes wrong, all that may remain is the cacophony.

Current features

Scan common contiguous EMDF containers in E-AC-3 sync frames.
Parse ID14 dense JOC parameters, Huffman data, differential matrices, and joc_clipgain.
Parse ID11 OAMD position updates and build object trajectories.
Reconstruct LFE plus 15 object channels through analysis QMF, parameter interpolation, the object matrix, and inverse QMF.
Write a 25-channel ADM BWF: a 10-channel 7.1.2 bed (silent except for LFE) plus 15 objects.
Render directly to 2.0, 3.1, 5.1, 7.1, 5.1.2, 5.1.4, 7.1.2, 7.1.4, 9.1.4, or 9.1.6.
Run public SOFA binaural rendering directly from pcm16 + ID11/OAMD, without a temporary ADM BWF.
Keep the binaural DSP in float64/complex128, including 961-sample latency compensation, cross-frame state, and the room tail.
Use a shared float32/PCM24 WAV writer and explicit PCM24 clipping policy for direct outputs.
Use the NumPy backend or an optional C++20 core through ctypes; auto falls back to Python when the native library is unavailable.
Read or write metadata sidecars and produce metadata, timing, and output reports.

Processing flow

M4A / E-AC-3
  ├─ FFmpeg extracts E-AC-3 and decodes the core 5.1 PCM
  ├─ EMDF → ID14 JOC parameters → object matrix
  ├─ core PCM → analysis QMF → parameter interpolation → inverse QMF
  ├─ ID11 OAMD → object positions and timing
  └─ LFE + 15 objects
       ├─ 25ch ADM BWF
       ├─ speaker WAV for the selected layout
       └─ direct ID11 timeline + SOFA HRTF → binaural WAV

The Python and C++ backends follow the same mathematics for JOC object reconstruction and speaker rendering. The public SOFA binaural backend currently runs in Python; bitstream parsing, the OAMD timeline, and CLI behavior also remain in Python.

Requirements

Python 3.10+
NumPy 1.24+
h5py 3.8+
SciPy 1.10+
A standalone FFmpeg executable; ffmpeg-python is not required. FFmpeg is discovered through PATH by default or selected with --ffmpeg
Optional: CMake and a C++20 toolchain to build the native core

Install the Python dependency in a project-specific environment:

python -m pip install -r requirements.txt

If FFmpeg is not on PATH:

python main.py input.m4a --ffmpeg C:\path\to\ffmpeg.exe

Usage

Write a 25-channel ADM BWF by default:

python main.py input.m4a

Select a backend or output path:

python main.py input.eac3 -o output.adm.wav --backend python
python main.py input.m4a --backend native --native-threads 2
python main.py input.m4a --native-library lib/eac3joc_core.dll

Write a speaker-layout WAV directly:

python main.py input.m4a --speaker-layout 2.0 --speaker-format float32
python main.py input.m4a --speaker-layout 5.1 --speaker-format int24
python main.py input.m4a --speaker-layout 7.1.2 --speaker-output output.7.1.2.wav

Write binaural stereo directly (ordinary objects are Near/Mid/Far only; Mid is the default). The HRTF input accepts three sources:

# 1) SOFA (defaults to HRTF/binaural.sofa, or an explicit path)
python main.py input.m4a --binaural
python main.py input.m4a --binaural --sofa-hrtf C:\HRTF\subject.sofa

# 2) Rosella .personalized_headphone (defaults to HRTF/binaural.personalized_headphone)
python main.py input.m4a --binaural --personalized-headphone
python main.py input.m4a --binaural --personalized-headphone C:\HRTF\subject.personalized_headphone

# 3) .jochrtf compiled cache
python main.py input.m4a --binaural --compiled-hrtf-cache C:\HRTF\subject.jochrtf

# Common options
python main.py input.m4a --binaural --sofa-hrtf C:\HRTF\subject.sofa `
  --binaural-mode near
python main.py input.m4a --binaural --sofa-hrtf C:\HRTF\subject.sofa `
  --hrtf-cache-policy disk
python main.py input.m4a --binaural --binaural-output output.binaural.wav

With none of the three specified, resolution tries, in order: HRTF/binaural.sofa, the unique .jochrtf under output/hrtf-cache, then HRTF/binaural.personalized_headphone; if none exist, an error asks for an explicit path.

.sofa is the portable source of truth; it can hold self-scanned or any generic HRTF data.
.personalized_headphone is a model produced by Dolby's official personalization scan; its JSON parsing is implemented by this project (src/rosella_model.py) and does not invoke any Dolby software.
.jochrtf is a project-internal cache compiled from SOFA; it is disposable, rebuildable, and written to output/hrtf-cache by default.

HRTF data lives under HRTF/ (git-ignored): the default SOFA HRTF/binaural.sofa and the default model HRTF/binaural.personalized_headphone. Because the cache contains transformed HRTF data, its use and redistribution remain subject to the source dataset's terms. See Binaural Rendering and Third-party notices for format boundaries, formulas, state, timing, and distribution considerations.

Speaker and binaural output share peak analysis, the WAV writer, and clipping policy. When PCM24 may clip in a non-interactive environment, select a policy explicitly:

python main.py input.m4a --speaker-layout 5.1 --speaker-format int24 --clip-action abort
python main.py input.m4a --speaker-layout 5.1 --speaker-format int24 --clip-action float32
python main.py input.m4a --speaker-layout 5.1 --speaker-format int24 --clip-action continue
python main.py input.m4a --binaural --sofa-hrtf C:\HRTF\subject.sofa `
  --binaural-format int24 --clip-action abort

Metadata and diagnostics:

python main.py input.m4a --print-metadata summary
python main.py input.m4a --metadata-only --print-metadata frames
python main.py input.m4a --metadata-cache metadata_cache
python main.py input.m4a --metadata-dir metadata_cache

Binaural render mode

--binaural-mode off|near|mid|far selects the binaural render mode; the default is mid, and both outputs share this single option:

Direct binaural rendering (--binaural): off is rejected (error); near/mid/far apply, defaulting to mid;
ADM BWF: the low 3 binaural-render-mode bits of the last 15 JOC object entries in DBMD segment 10 carry off=0/near=1/far=2/mid=3, leaving the first 10 bed entries unchanged; the default is mid, and off explicitly disables the binaural metadata hint.

python main.py input.m4a --binaural-mode mid
python main.py input.m4a --binaural-mode off   # ADM BWF only: disable the DBMD hint

The default mid is a human-specified rendering hint; it is not original binaural metadata extracted or recovered from the input E-AC-3 JOC bitstream, nor does it represent the original mix's per-object binaural settings. The hint does not change PCM, object trajectories, or direct speaker rendering. The adjacent .report.json records binaural_mode (the mode name) and binaural_mode_value (the ADM code; null for direct binaural output).

OAMD time alignment

Object trajectories and direct speaker rendering both default to a metadata delay of 1473 samples. This value describes the theoretical mapping between decoder-output PCM and OAMD updates. The speaker renderer retains its existing 32-sample control block, so the default update lands on effective block boundary 1472:

align32(1473) = 1472

Override the two paths with --object-delay-samples and --speaker-metadata-offset, respectively. The 1473-sample timing offset is distinct from the 640-value inverse-QMF filter/window state; 640 is a QMF state length, not a metadata delay.

The direct binaural path uses --object-delay-samples. Each ID11/OAMD event is placed on an absolute sample timeline from its frame start, outer-subpayload offset, and block offset, then shifted by that delay. Each 1536-sample input frame is processed as three consecutive 512-sample blocks; the interpolated position, direction, and profile are updated at each block's absolute starting sample.

Binaural calculation

See Binaural Rendering Mathematics for QMF, hybrid processing, direction fields, distance, ITD, room processing, the 512-sample parameter updates above, and 961-sample latency compensation.

For all options:

python main.py --help

Without -o, output still goes to output/ at the repository root. The directory move intentionally preserves this behavior.

Native core

The repository does not include native binaries by default. Download a prebuilt runtime for the current platform from a project Release, or build one locally, then place the runtime library under lib/ at the repository root; create the directory if it is absent. To build it yourself, run CMake from the repository root:

cmake -S native -B build/cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX="$PWD/lib"
cmake --build build/cmake --config Release
cmake --install build/cmake --config Release

The runtime lookup order is:

--native-library;
EAC3JOC_NATIVE_LIBRARY;
the standard platform library name under lib/.

See the native-core notes for ABI, state, and precision details.

Repository layout

JustOneCacophony/
├─ main.py               command-line entry point
├─ src/                  Python implementation modules
├─ native/               C/C++ acceleration core, C ABI, and required table data
├─ data/                 Python runtime table data
├─ lib/                  native runtime drop-in directory (create as needed)
├─ HRTF/                 user HRTF data directory (create as needed, git-ignored)
├─ output/               output directory (create as needed; the .jochrtf cache defaults to its hrtf-cache subdirectory)
├─ docs/                 math and native-core notes in both languages
├─ requirements.txt      Python dependency
├─ README.md             Chinese documentation
└─ README.en.md          English documentation

Mathematical implementation

The main documented stages are:

dense JOC differential reconstruction and dequantization;
parameter-band mapping to 64 QMF subbands;
cross-frame parameter interpolation;
analysis/inverse QMF, surround delay, and FIR state;
the 1217-sample LFE delay;
OAMD Q15 coordinate conversion;
equal-power panning over target-layout regions;
layout-dependent position compensation and sample-wise gain ramps;
float32 and PCM24 output quantization;
SOFA canonical import, 64-QMF/77-hybrid projection, 36×2×77 fifth-order fields, exactly-once delay/phase, project early/late room behavior, and special LFE.

See the mathematical notes for the equations used by the decoding and rendering process.

Known limitations

Only the common contiguous EMDF transport is covered. Fragmented transport across multiple audio-block skip fields is not covered.
Dense JOC is the main path. The Sparse JOC branch should not be treated as supported.
The speaker and SOFA binaural paths currently cover ordinary point objects; extent, spread, diffuse, divergence, channel lock, and similar controls are outside the supported scope.
OAMD trim elements are boundary-checked and skipped; warp, balance, and trim parameters are not applied to raw object trajectories or speaker rendering.
Multi-data-point streams, uncommon band configurations, and unusual OAMD scheduling have less coverage than common 12-band, single-data-point material.
A speaker limiter is outside the current primary formula.
The SOFA importer currently supports the strict SimpleFreeFieldHRIR FIR subset; other SOFA conventions require explicit adapters.
The binaural runtime is fixed at 48 kHz, fifth order, and one measurement-radius shell at a time; the public binaural backend defaults to the native accelerator and falls back to Python when the native library is unavailable.
ADM output, native binaries, speaker layouts, and binaural models still need broader interoperability checks across platforms, players, and real material.

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JustOneCacophony — JOC