FA-11021 / Audio channel mixing / Open access
Mid side decode reconstruction · case 01
Decoder halves amplitudes even though normalization was already applied during encoding.
ROOT CAUSE
Decoder halves amplitudes even though normalization was already applied during encoding.
VERIFIED REPAIR
Apply the explicit PCM/playback contract: Decode normalized mid/side frames into left=mid+side and right=mid-side, exactly inverting the half-scaled encoder.
Unsuccessful approach: Reversing only the right reconstruction inverts that output channel polarity.
Case contract
Decode normalized mid/side frames into left=mid+side and right=mid-side, exactly inverting the half-scaled encoder.
Why this case matters
A pure Python local audio pipeline stage with explicit sample formats and frame conventions; no real-time device or signal-spectrum claims.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(frames):
return [[(mid+side)/2,(mid-side)/2] for mid,side in frames]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([[1, 0]],)),[[1, 1]])
check('fixture 2',solve(*([[0, 1]],)),[[1, -1]])
check('fixture 3',solve(*([[0.5, -0.5]],)),[[0, 1]])
check('fixture 4',solve(*([],)),[])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| fixture 1 | [[0.5, 0.5]] | [[1, 1]] | Failed |
| fixture 2 | [[0.5, -0.5]] | [[1, -1]] | Failed |
| fixture 3 | [[0.0, 0.5]] | [[0, 1]] | Failed |
| fixture 4 | [] | [] | Passed |
SHA-256 / 1d8537934851a0d57c0c5f76b154dc54807017b3c65b36746c9b6cc7bba6a7c7
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(frames):
return [[mid+side,side-mid] for mid,side in frames]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([[1, 0]],)),[[1, 1]])
check('fixture 2',solve(*([[0, 1]],)),[[1, -1]])
check('fixture 3',solve(*([[0.5, -0.5]],)),[[0, 1]])
check('fixture 4',solve(*([],)),[])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| fixture 1 | [[1, -1]] | [[1, 1]] | Failed |
| fixture 2 | [[1, 1]] | [[1, -1]] | Failed |
| fixture 3 | [[0.0, -1.0]] | [[0, 1]] | Failed |
| fixture 4 | [] | [] | Passed |
SHA-256 / e0474615dbb74f4f5afd85cc73a25308df37577912ac59414f4a27d7c4c24b8f
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(frames):
return [[mid+side,mid-side] for mid,side in frames]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([[1, 0]],)),[[1, 1]])
check('fixture 2',solve(*([[0, 1]],)),[[1, -1]])
check('fixture 3',solve(*([[0.5, -0.5]],)),[[0, 1]])
check('fixture 4',solve(*([],)),[])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| fixture 1 | [[1, 1]] | [[1, 1]] | Passed |
| fixture 2 | [[1, -1]] | [[1, -1]] | Passed |
| fixture 3 | [[0.0, 1.0]] | [[0, 1]] | Passed |
| fixture 4 | [] | [] | Passed |
SHA-256 / e02e8a14efce584e815c39dff5d37ba024de216f81f9c511760457629e9b209a
Verification & scope
This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:38:44.302222+00:00.
Case digest / 330153a4522e55f3edeb4385c091c534ebd44c2f5fb0e66303427a694e60f7da