feat(analysis): implement DSP-based harmonic and pitch pipelines (#107)#112
Merged
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- Added ChordRecognizer using librosa's chromagrams to extract chords from stems. - Added PitchTracker using librosa's pYIN/YIN to determine pitch ranges. - Updated RoleExtractor to use real stems and these new DSP components to inject actual chords and ranges into roles. - Reached 100% test coverage with extensive mocking of librosa functions to simulate error conditions. - Resolved type hinting and formatting issues.
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📝 WalkthroughSummary by CodeRabbit릴리스 노트
워크스루새로운 변경사항
시퀀스 다이어그램sequenceDiagram
participant RoleExtractor
participant PitchTracker
participant ChordRecognizer
participant librosa
RoleExtractor->>RoleExtractor: extract() with audio_features + stems
RoleExtractor->>RoleExtractor: Dynamic import PitchTracker & ChordRecognizer
RoleExtractor->>PitchTracker: track(stems["vocals"], sr=22050)
PitchTracker->>librosa: pyin() for pitch estimation
librosa-->>PitchTracker: f0, voiced frames
PitchTracker-->>RoleExtractor: TrackedPitchRange (vocal_range)
RoleExtractor->>PitchTracker: track(stems["bass"], sr=22050)
PitchTracker->>librosa: pyin() for pitch estimation
librosa-->>PitchTracker: f0, voiced frames
PitchTracker-->>RoleExtractor: TrackedPitchRange (bass_range)
RoleExtractor->>ChordRecognizer: recognize(stems["bass"], sr=22050)
ChordRecognizer->>librosa: chroma_cqt() + HPSS filtering
librosa-->>ChordRecognizer: chromagram
ChordRecognizer-->>RoleExtractor: list[TrackedChord] (bass_chord)
RoleExtractor->>ChordRecognizer: recognize(stems["other"], sr=22050)
ChordRecognizer->>librosa: chroma_cqt() + HPSS filtering
librosa-->>ChordRecognizer: chromagram
ChordRecognizer-->>RoleExtractor: list[TrackedChord] (vocal_chord)
RoleExtractor->>RoleExtractor: Assemble roles with extracted ranges & harmony
RoleExtractor-->>RoleExtractor: Return RoleExtractionResult
예상 코드 리뷰 노력🎯 3 (보통) | ⏱️ ~20분 관련된 PR
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Actionable comments posted: 9
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/plans/2026-03-28-ml-engine-integration.md`:
- Line 20: 제목 줄 "### Track 3: Harmonic & Pitch Pipelines (`#107`) (COMPLETED)" 바로
다음에 빈 줄을 추가하여 Markdown 제목 아래에 공백을 넣어 주세요 (markdownlint MD022 경고 해결). 파일의 해당 헤더
텍스트를 찾아 그 다음 줄에 하나의 빈 줄을 삽입하면 됩니다.
In `@services/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.py`:
- Around line 85-86: librosa.decompose.nn_filter 호출이 try/except 밖에 있어
chromagram이 비정상일 때 전체 처리가 중단될 수 있으니 nn_filter(chromagram, aggregate=np.median,
metric="cosine") 호출을 try/except로 감싸고 예외 발생 시 processLogger 또는 로깅 시스템에 에러를 기록한 뒤
원래의 chromagram을 유지하거나 대체(예: smoothing 건너뛰기)하도록 처리하십시오; 대상 심볼: chromagram 변수와
librosa.decompose.nn_filter, aggregate=np.median, metric="cosine"를 수정하여 안전하게
재할당하거나 예외 시 그대로 반환하도록 구현하세요.
- Around line 118-129: Extract the magic numbers used in the decision block
(max_sim < 0.3, rms_val < 0.01, chroma_var < 0.02) into named constants or class
attributes so they are configurable and documented; for example define
UNVOICED_SIM_THRESHOLD, RMS_THRESHOLD, CHROMA_VAR_THRESHOLD (as module-level
constants or on the ChordRecognizer class as self.UNVOICED_SIM_THRESHOLD) and
replace the inline literals in the chord detection block in chord_recognizer.py,
preserving the current defaults and add a short docstring or comment describing
each threshold and recommended tuning guidance.
In `@services/analysis-engine/src/bandscope_analysis/roles/extractor.py`:
- Around line 68-71: Change the vocal_range fallback so missing notes are
preserved as None instead of an empty string: in extractor.py set "lowestNote":
p_res["lowest_note"] and "highestNote": p_res["highest_note"] (no "or ''"
fallback) so analyzer._parse_note() receives None and can apply explicit
handling; update any callers expecting "" to handle None (or raise) and add a
unit test for missing lowest_note/highest_note to assert explicit rejection or
proper error behavior.
In `@services/analysis-engine/tests/test_chord_recognizer.py`:
- Around line 47-48: There are three consecutive blank lines between top-level
functions in services/analysis-engine/tests/test_chord_recognizer.py; reduce
them to two blank lines to comply with PEP 8 (ensure at most two blank lines
between top-level function and class definitions, e.g., between the surrounding
test functions in this file).
- Line 24: Remove the leftover debug print statement from the test by deleting
the line print("RESULT:", result) in the test function (where variable result is
asserted); ensure the test only uses assertions (no console output) so the test
suite remains clean and deterministic.
In `@services/analysis-engine/tests/test_pitch_tracker.py`:
- Around line 70-73: Remove the excessive empty lines in
tests/test_pitch_tracker.py: there are four consecutive blank lines between two
top-level test functions; conform to PEP 8 by reducing them to at most two blank
lines between the adjacent test functions (i.e., the two surrounding def test_*
functions) so only one or two blank lines remain separating those function
definitions.
In `@services/analysis-engine/tests/test_roles_ml.py`:
- Around line 30-35: The test double side_effect_track returns None for
unmatched inputs which mismatches the real PitchTracker.track() return type (a
TrackedPitchRange dict); update side_effect_track in the test to always return a
dict with the same keys (e.g. {"lowest_note": None, "highest_note": None} or
suitable default note strings) instead of None so the stub matches
PitchTracker.track()’s shape and types.
- Line 1: The test module services/analysis-engine/tests/test_roles_ml.py is
missing a module-level docstring (D100); add a short descriptive docstring at
the top of the file (above the import lines) that states the purpose of the
tests in this file (e.g., what behavior or component the tests cover such as
role-related ML logic) so the module has a proper top-level docstring.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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Configuration used: Organization UI
Review profile: ASSERTIVE
Plan: Pro
Run ID: 43572482-428f-41a4-8cb7-36d2f02bdc54
📒 Files selected for processing (7)
docs/plans/2026-03-28-ml-engine-integration.mdservices/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.pyservices/analysis-engine/src/bandscope_analysis/ranges/pitch_tracker.pyservices/analysis-engine/src/bandscope_analysis/roles/extractor.pyservices/analysis-engine/tests/test_chord_recognizer.pyservices/analysis-engine/tests/test_pitch_tracker.pyservices/analysis-engine/tests/test_roles_ml.py
| - **Output**: 4 or 6 discrete stems (vocals, bass, drums, other). | ||
|
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||
| ### Track 3: Harmonic & Pitch Pipelines (#107) | ||
| ### Track 3: Harmonic & Pitch Pipelines (#107) (COMPLETED) |
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Markdown 포맷팅: 제목 아래 빈 줄 필요
정적 분석 도구(markdownlint MD022)에서 제목 아래에 빈 줄이 필요하다고 경고합니다.
📝 포맷팅 수정 제안
### Track 3: Harmonic & Pitch Pipelines (`#107`) (COMPLETED)
+
- **Goal**: Replace hardcoded `C#m7` strings with DSP-derived chord and pitch arrays.📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| ### Track 3: Harmonic & Pitch Pipelines (#107) (COMPLETED) | |
| ### Track 3: Harmonic & Pitch Pipelines (`#107`) (COMPLETED) | |
| - **Goal**: Replace hardcoded `C#m7` strings with DSP-derived chord and pitch arrays. |
🧰 Tools
🪛 markdownlint-cli2 (0.22.0)
[warning] 20-20: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Below
(MD022, blanks-around-headings)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@docs/plans/2026-03-28-ml-engine-integration.md` at line 20, 제목 줄 "### Track
3: Harmonic & Pitch Pipelines (`#107`) (COMPLETED)" 바로 다음에 빈 줄을 추가하여 Markdown 제목
아래에 공백을 넣어 주세요 (markdownlint MD022 경고 해결). 파일의 해당 헤더 텍스트를 찾아 그 다음 줄에 하나의 빈 줄을
삽입하면 됩니다.
Comment on lines
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| # Optional: apply temporal smoothing to chromagram to reduce noise | ||
| chromagram = librosa.decompose.nn_filter(chromagram, aggregate=np.median, metric="cosine") |
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nn_filter 호출에 대한 예외 처리 누락
librosa.decompose.nn_filter 호출이 try/except 블록 외부에 있습니다. chromagram이 비정상적인 형태일 경우 예외가 발생할 수 있으며, 이는 전체 인식 과정을 실패시킬 수 있습니다.
🛡️ 예외 처리 추가 제안
# Optional: apply temporal smoothing to chromagram to reduce noise
- chromagram = librosa.decompose.nn_filter(chromagram, aggregate=np.median, metric="cosine")
+ try:
+ chromagram = librosa.decompose.nn_filter(chromagram, aggregate=np.median, metric="cosine")
+ except Exception:
+ pass # Use unsmoothed chromagram if filtering fails🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.py`
around lines 85 - 86, librosa.decompose.nn_filter 호출이 try/except 밖에 있어
chromagram이 비정상일 때 전체 처리가 중단될 수 있으니 nn_filter(chromagram, aggregate=np.median,
metric="cosine") 호출을 try/except로 감싸고 예외 발생 시 processLogger 또는 로깅 시스템에 에러를 기록한 뒤
원래의 chromagram을 유지하거나 대체(예: smoothing 건너뛰기)하도록 처리하십시오; 대상 심볼: chromagram 변수와
librosa.decompose.nn_filter, aggregate=np.median, metric="cosine"를 수정하여 안전하게
재할당하거나 예외 시 그대로 반환하도록 구현하세요.
Comment on lines
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| # Simple threshold for unvoiced/noise (if max similarity is very low) | ||
| max_sim = similarity[match, i] | ||
| rms_val = rms[i] if i < len(rms) else 0.0 | ||
|
|
||
| # For noise, the max similarity is usually lower, but to be robust | ||
| # we should check if the chromagram is too flat (e.g. low variance) | ||
| # or if the RMS energy is really low. | ||
| # However, since dot product normalization makes noise match *something*, | ||
| # we can look at the variance of the chromagram frame. | ||
| chroma_var = np.var(chromagram[:, i]) | ||
| if max_sim < 0.3 or rms_val < 0.01 or chroma_var < 0.02: | ||
| chord_label = "N" |
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🧹 Nitpick | 🔵 Trivial
매직 넘버에 대한 문서화 권장
max_sim < 0.3, rms_val < 0.01, chroma_var < 0.02 등의 임계값이 하드코딩되어 있습니다. 이 값들은 도메인별 튜닝이 필요할 수 있으므로, 클래스 속성이나 상수로 정의하고 문서화하는 것이 좋습니다.
♻️ 상수 추출 제안
class ChordRecognizer:
"""Extracts chords from audio data."""
+ # Noise detection thresholds (may require tuning per domain)
+ SIMILARITY_THRESHOLD = 0.3
+ RMS_THRESHOLD = 0.01
+ CHROMA_VARIANCE_THRESHOLD = 0.02
+
def __init__(self) -> None:- if max_sim < 0.3 or rms_val < 0.01 or chroma_var < 0.02:
+ if (max_sim < self.SIMILARITY_THRESHOLD or
+ rms_val < self.RMS_THRESHOLD or
+ chroma_var < self.CHROMA_VARIANCE_THRESHOLD):
chord_label = "N"🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/src/bandscope_analysis/chords/chord_recognizer.py`
around lines 118 - 129, Extract the magic numbers used in the decision block
(max_sim < 0.3, rms_val < 0.01, chroma_var < 0.02) into named constants or class
attributes so they are configurable and documented; for example define
UNVOICED_SIM_THRESHOLD, RMS_THRESHOLD, CHROMA_VAR_THRESHOLD (as module-level
constants or on the ChordRecognizer class as self.UNVOICED_SIM_THRESHOLD) and
replace the inline literals in the chord detection block in chord_recognizer.py,
preserving the current defaults and add a short docstring or comment describing
each threshold and recommended tuning guidance.
Comment on lines
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| vocal_range = { | ||
| "lowestNote": p_res["lowest_note"] or "", | ||
| "highestNote": p_res["highest_note"] or "", | ||
| } |
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🧩 Analysis chain
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빈 문자열 기본값이 조용히 C4로 변환되어 부정확한 범위 감지를 초래할 수 있습니다
lowest_note가 None일 때 빈 문자열로 fallback하면, 범위 분석 함수의 _parse_note()에서 이를 감지하고 중음(C4)의 기본값으로 변환합니다(line 55 in analyzer.py). 이 조용한 기본값 변환은:
- 실제 데이터 누락을 감지할 수 없게 함
- 여러 범위가 C4로 기본값이 되면 거짓 양성 겹침 감지 가능
- 불완전한 입력에 대한 테스트 커버리지 부재
None 값을 명시적으로 거부하거나 기본값 동작에 대한 테스트를 추가하는 것이 좋습니다.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/src/bandscope_analysis/roles/extractor.py` around
lines 68 - 71, Change the vocal_range fallback so missing notes are preserved as
None instead of an empty string: in extractor.py set "lowestNote":
p_res["lowest_note"] and "highestNote": p_res["highest_note"] (no "or ''"
fallback) so analyzer._parse_note() receives None and can apply explicit
handling; update any callers expecting "" to handle None (or raise) and add a
unit test for missing lowest_note/highest_note to assert explicit rejection or
proper error behavior.
| np.random.seed(42) | ||
| y = np.random.randn(22050 * 2) * 0.1 | ||
| result = recognizer.recognize(y, sr=22050) | ||
| print("RESULT:", result) |
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디버그 print 문 제거 필요
테스트 코드에 디버그용 print 문이 남아 있습니다. 프로덕션 테스트 스위트에서는 제거해야 합니다.
🧹 print 문 제거
result = recognizer.recognize(y, sr=22050)
- print("RESULT:", result)
# Could be N (No chord) or empty🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/tests/test_chord_recognizer.py` at line 24, Remove
the leftover debug print statement from the test by deleting the line
print("RESULT:", result) in the test function (where variable result is
asserted); ensure the test only uses assertions (no console output) so the test
suite remains clean and deterministic.
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Contributor
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🧹 Nitpick | 🔵 Trivial
연속된 빈 줄 정리
함수 사이에 3개의 빈 줄이 있습니다. PEP 8 권장에 따라 2개로 줄이는 것이 좋습니다.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/tests/test_chord_recognizer.py` around lines 47 -
48, There are three consecutive blank lines between top-level functions in
services/analysis-engine/tests/test_chord_recognizer.py; reduce them to two
blank lines to comply with PEP 8 (ensure at most two blank lines between
top-level function and class definitions, e.g., between the surrounding test
functions in this file).
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Contributor
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🧹 Nitpick | 🔵 Trivial
불필요한 빈 줄 제거
연속된 4개의 빈 줄이 있습니다. PEP 8 권장에 따라 함수 사이에는 2개의 빈 줄이 적절합니다.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/tests/test_pitch_tracker.py` around lines 70 - 73,
Remove the excessive empty lines in tests/test_pitch_tracker.py: there are four
consecutive blank lines between two top-level test functions; conform to PEP 8
by reducing them to at most two blank lines between the adjacent test functions
(i.e., the two surrounding def test_* functions) so only one or two blank lines
remain separating those function definitions.
| @@ -0,0 +1,123 @@ | |||
| from unittest.mock import patch | |||
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파이프라인 실패: 모듈 docstring 누락 (D100)
CI 파이프라인에서 ruff check가 실패했습니다. 모듈 수준 docstring을 추가해야 합니다.
📝 docstring 추가 제안
+"""Tests for RoleExtractor with audio-driven feature extraction."""
+
from unittest.mock import patch📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| from unittest.mock import patch | |
| """Tests for RoleExtractor with audio-driven feature extraction.""" | |
| from unittest.mock import patch |
🧰 Tools
🪛 GitHub Actions: ci
[error] 1-1: ruff check (D100): Missing docstring in public module
🪛 GitHub Actions: release
[error] 1-1: ruff check failed: D100 Missing docstring in public module. (Found 1 error.)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/tests/test_roles_ml.py` at line 1, The test module
services/analysis-engine/tests/test_roles_ml.py is missing a module-level
docstring (D100); add a short descriptive docstring at the top of the file
(above the import lines) that states the purpose of the tests in this file
(e.g., what behavior or component the tests cover such as role-related ML logic)
so the module has a proper top-level docstring.
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| def side_effect_track(y, sr): | ||
| if y is vocals_stem: | ||
| return {"lowest_note": "A3", "highest_note": "A4"} | ||
| elif y is bass_stem: | ||
| return {"lowest_note": "E1", "highest_note": "E2"} | ||
| return None |
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side_effect 함수에서 None 반환 시 타입 불일치 가능
side_effect_track 함수가 매칭되지 않는 경우 None을 반환하지만, 실제 PitchTracker.track()은 항상 TrackedPitchRange dict를 반환합니다. 이로 인해 테스트가 실제 동작과 다른 시나리오를 검증할 수 있습니다.
♻️ 일관된 반환 타입 제안
def side_effect_track(y, sr):
if y is vocals_stem:
return {"lowest_note": "A3", "highest_note": "A4", "confidence": "high"}
elif y is bass_stem:
return {"lowest_note": "E1", "highest_note": "E2", "confidence": "high"}
- return None
+ return {"lowest_note": None, "highest_note": None, "confidence": "low"}🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@services/analysis-engine/tests/test_roles_ml.py` around lines 30 - 35, The
test double side_effect_track returns None for unmatched inputs which mismatches
the real PitchTracker.track() return type (a TrackedPitchRange dict); update
side_effect_track in the test to always return a dict with the same keys (e.g.
{"lowest_note": None, "highest_note": None} or suitable default note strings)
instead of None so the stub matches PitchTracker.track()’s shape and types.
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Summary
ChordRecognizerusing librosa's chromagrams to extract chords from stems.PitchTrackerusing librosa's pYIN/YIN to determine pitch ranges.RoleExtractorto dynamically extract ranges and chords when audio stems are provided.Testing
Ran
uv run pytest --covresulting in 100% coverage. All librosa components were mocked and edge cases gracefully handled.