FA-10861 / Audio frame buffers / Open access
Mono to multichannel duplication · case 01
Duplicating the whole waveform produces channel-major storage.
ROOT CAUSE
Duplicating the whole waveform produces channel-major storage.
VERIFIED REPAIR
Apply the explicit PCM/playback contract: Expand each mono sample to a frame of nonnegative channel count; output is frame-major.
Unsuccessful approach: Keeping singleton frames ignores the requested output channel count.
Case contract
Expand each mono sample to a frame of nonnegative channel count; output is frame-major.
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(samples, channels):
return [list(samples) for i in range(channels)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([1, 2], 3)),[[1, 1, 1], [2, 2, 2]])
check('fixture 2',solve(*([], 2)),[])
check('fixture 3',solve(*([5], 1)),[[5]])
check('fixture 4',solve(*([1, 2], 0)),[[], []])
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, 2], [1, 2], [1, 2]] | [[1, 1, 1], [2, 2, 2]] | Failed |
| fixture 2 | [[], []] | [] | Failed |
| fixture 3 | [[5]] | [[5]] | Passed |
| fixture 4 | [] | [[], []] | Failed |
SHA-256 / a724fd5cb11080c6b24a429c72f62d39435b175c31a66804cad5167d488b7873
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(samples, channels):
return [[x] for x in samples]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([1, 2], 3)),[[1, 1, 1], [2, 2, 2]])
check('fixture 2',solve(*([], 2)),[])
check('fixture 3',solve(*([5], 1)),[[5]])
check('fixture 4',solve(*([1, 2], 0)),[[], []])
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], [2]] | [[1, 1, 1], [2, 2, 2]] | Failed |
| fixture 2 | [] | [] | Passed |
| fixture 3 | [[5]] | [[5]] | Passed |
| fixture 4 | [[1], [2]] | [[], []] | Failed |
SHA-256 / 907735aa36c9c8fad5f8b6d573b7a17b7dfd6bf6459e4b2090b93352306ba757
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(samples, channels):
return [[x]*channels for x in samples]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1',solve(*([1, 2], 3)),[[1, 1, 1], [2, 2, 2]])
check('fixture 2',solve(*([], 2)),[])
check('fixture 3',solve(*([5], 1)),[[5]])
check('fixture 4',solve(*([1, 2], 0)),[[], []])
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], [2, 2, 2]] | [[1, 1, 1], [2, 2, 2]] | Passed |
| fixture 2 | [] | [] | Passed |
| fixture 3 | [[5]] | [[5]] | Passed |
| fixture 4 | [[], []] | [[], []] | Passed |
SHA-256 / dc57b8f7e054a57b10f30472816b5e2c4387b00ebf707cb76c6b4fcfa78dc552
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 2026-09-29T14:38:42.912153+00:00.
Case digest / c00d15c7d86c7c9a0c9b9df49b8f218e73d8c770c3e87537475e09bb4be129d0