⚡ Vectorize chromagram variance calculation in chord recognizer

[seonghobae]

💡 What: Vectorized the variance calculation of the chromagram across the time axis (axis 0), which computes the variance once for all frames, replacing the unvectorized np.var(chromagram[:, i]) inside the audio frame loop.
🎯 Why: To avoid the redundant array slicing and function call overhead that was slowing down the chord recognition processing loop. Calculating this out-of-band across the array in numpy's C-implementation allows it to process extremely quickly compared to looping in Python.
📊 Measured Improvement: The loop segment benchmark showed execution time drop from ~5.27 seconds to ~0.28 seconds for a simulated 60s audio file over 100 runs. This is roughly an 18x performance boost in the iteration section of chord recognition. The full functionality exactly matches previous behaviors and all tests pass cleanly.

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PR created automatically by Jules for task 13733934016029207333 started by @seonghobae

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ℹ️ Recent review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro

Run ID: 685bac6b-fefb-481e-8150-c98e7f1e3ab8

📥 Commits

Reviewing files that changed from the base of the PR and between 4232bb8 and 0ffdf3a.

📒 Files selected for processing (1)

• services/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.py

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📝 Walkthrough

Summary by CodeRabbit

릴리스 노트

• 성능 최적화
  • 코드 호출 반복을 제거하여 음성 분석 성능을 개선했습니다.

둘러보기

Chord recognizer의 프레임 처리 루프에서 chroma 분산 계산을 반복으로 수행하던 방식을 변경했습니다. 루프 진입 전 np.var(chromagram, axis=0)로 모든 프레임의 분산을 한 번에 계산하여 저장하고, 루프 내부에서는 사전 계산된 값을 참조하도록 최적화했습니다.

변경 사항

Chroma 분산 계산 최적화

계층 / 파일	설명
Chroma 분산 사전 계산 및 참조 services/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.py	루프 진입 전에 np.var(chromagram, axis=0)로 프레임별 분산을 한 번만 계산하여 chroma_vars에 저장하고, 루프 내부에서 chroma_vars[i]로 참조하여 프레임 반복 계산을 제거했습니다.

예상 코드 리뷰 난이도

🎯 1 (매우 간단함) | ⏱️ ~3분

관련 PR

• Seongho-Bae/bandscope#272: 두 PR 모두 ChordRecognizer의 chord_recognizer.py를 수정합니다. 이 PR은 chroma 분산 계산 방식을 최적화하고, 관련 PR은 노이즈/침묵 판정에 사용되는 분산 값을 참조하는 segment 생성 흐름을 리팩터링합니다.

제안 레이블

codex

🐰 체인지로그 축하시

Chroma 분산을 한 번만 계산하고
루프는 저장된 값을 재밌게 참조하네!
반복 계산 제거해
더 빠르고 깔끔한 코드,
효율의 승리입니다! 🎵✨

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	제목은 PR의 주요 변경사항인 chromagram 분산 계산 벡터화를 명확하고 간결하게 요약하고 있습니다.
Description check	✅ Passed	설명은 변경사항의 내용(벡터화), 이유(성능 개선), 측정된 개선사항(18배 성능 향상)을 구체적으로 다루고 있어 변경 내용과 관련이 있습니다.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 100.00%.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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✨ Finishing Touches

📝 Generate docstrings

• Create stacked PR
• Commit on current branch

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch perf/chord-recognizer-vectorization-13733934016029207333

✨ Simplify code

• Create PR with simplified code
• Commit simplified code in branch perf/chord-recognizer-vectorization-13733934016029207333

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[seonghobae]

@coderabbitai resolve

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✅ Action performed

Comments resolved and changes approved.

[seonghobae]

Merged latest develop and narrowed this PR to the production chord recognizer vectorization only.

Validation:

• uv run --project services/analysis-engine pytest services/analysis-engine/tests/test_chord_recognizer.py -q -> 10 passed.
• (cd services/analysis-engine && uv run ruff check src tests) -> passed.

Cleanup:

• Removed the standalone root benchmark.py artifact from the PR.
• Final PR diff is only services/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.py.

@coderabbitai approve

[coderabbitai]

@seonghobae Looks great — clean, focused diff with passing tests and linting. Approving!

(∩^ω^)⊃━☆ﾟ.*･｡ﾟ

✅ Action performed

Comments resolved and changes approved.