# TASK-009 ## Title Rhythm Analyzer --- ## Objective Implement the Rhythm Analyzer for AI MusiMuse. This analyzer extracts temporal rhythmic characteristics from decoded audio. The extracted features will become an important component of Music DNA and future similarity calculations. Do not implement harmony, embeddings, or machine learning. --- # Expected Commit Message Add rhythm analyzer --- # Requirements Read SPEC.md before implementing anything. Follow all AI Rules. Follow the Analysis Framework. Reuse existing DSP utilities whenever possible. Do not violate architectural boundaries. --- # Scope Create ``` srcanalyzerrhythm ``` Files ``` __init__.py rhythm_analyzer.py ``` Register the analyzer through ``` build_default_registry() ``` No CLI changes are required. The existing ``` musimuse analyze ``` must automatically execute this analyzer. --- # Analyzer Create ``` RhythmAnalyzer ``` inherits from ``` Analyzer ``` Name ``` rhythm ``` Version ``` 1.0.0 ``` --- # Shared DSP Create ``` srcdsprhythm.py ``` Implement reusable rhythm utilities. Minimum API ```python compute_onset_envelope() estimate_tempo() estimate_beat_period() compute_autocorrelation() compute_rhythm_density() ``` Future rhythm analyzers must reuse these functions. --- # Configuration Extend ``` srcdspconfig.py ``` with ```python ONSET_HOP_SIZE = 512 TEMPO_MIN_BPM = 40 TEMPO_MAX_BPM = 220 ``` No analyzer may hardcode these values. --- # Features Calculate --- ## Estimated Tempo Feature ``` tempo_bpm ``` Unit ``` BPM ``` --- ## Beat Period Feature ``` beat_period_seconds ``` Unit ``` seconds ``` --- ## Rhythm Density Feature ``` rhythm_density ``` Definition Estimated number of detected onsets per second. --- ## Beat Strength Feature ``` beat_strength ``` Range ``` 0.0 – 1.0 ``` Derived from normalized autocorrelation peak. --- ## Onset Count Feature ``` onset_count ``` Integer. --- ## Onset Rate Feature ``` onset_rate ``` Definition Detected onsets per second. --- ## Rhythm Regularity Feature ``` rhythm_regularity ``` Range ``` 0.0 – 1.0 ``` Higher values indicate more periodic rhythmic structure. --- # DSP Requirements Use - numpy - scipy.signal Use vectorized operations whenever possible. Reuse existing spectrum utilities if appropriate. --- # Channel Handling Mono Analyze directly. Stereo Convert to mono ``` (left + right) 2 ``` before analysis. --- # Numerical Stability Correctly handle - silence - constant signals - empty audio - NaN - Inf Raise ``` AnalysisError ``` when appropriate. --- # Persistence No database access. Pipeline automatically stores generated features. --- # Logging No logging inside the analyzer. --- # Tests Create tests covering - silence - metronome click track - regular pulse - irregular pulse - white noise - sine wave - mono - stereo - tempo estimation - onset detection - beat strength - onset count - onset rate - rhythm density - rhythm regularity - deterministic output - invalid input - pipeline integration - persistence integration --- # Forbidden Do NOT implement - beat tracking - beat alignment - downbeat detection - swing estimation - groove analysis - harmony - key detection - Music DNA - similarity - machine learning --- # Definition of Done ✓ RhythmAnalyzer implemented ✓ Registered through build_default_registry() ✓ Shared rhythm DSP utilities implemented ✓ DSP configuration extended ✓ Produces deterministic results ✓ Handles mono and stereo ✓ Handles silence correctly ✓ Handles invalid input safely ✓ Produces immutable FeatureSet ✓ Automatically persists features ✓ Tests pass ✓ Ruff passes ✓ Black passes ✓ isort passes --- # Architectural Notes This analyzer introduces temporal rhythm analysis. The shared DSP rhythm module becomes the project's reusable rhythm processing layer. Future beat tracking, groove analysis, and Music DNA must reuse these utilities rather than implementing rhythm estimation independently. The extracted rhythm descriptors intentionally remain deterministic and low-level. They are designed to become stable inputs for Music DNA and similarity search.