Add Python test infrastructure for tempo improvements

[Copilot]

Reviewed TEMPO_WORK_SUMMARY.md (3,690 lines documenting Phases 1-3C tempo improvements). Found all C implementation complete and tested (66/66 tests passing), but Python test coverage missing. Added comprehensive test suite validating the improved beat tracking is accessible via Python bindings.

Changes

Python Test Suite (test_tempo_improvements.py - 21 tests)

• API coverage: tempo priors, adaptive smoothing, tempogram, onset enhancement, multi-scale analysis
• Synthetic audio: click track generation at 80/120/140 BPM
• Real audio: test_bpm_changes.wav processing
• Genre presets: EDM, classical, hip-hop, drum & bass

Benchmark Validation (test_tempo_benchmark.py - 4 tests)

• Autocorrelation: 83.3% detection (5/6), 0.51 BPM avg error
• Multi-scale tempogram: 50.0% detection (3/6), 2.06 BPM avg error
• Matches C test performance exactly

Usage Examples (demo_tempo_improvements.py)

• Basic autocorrelation (default, recommended)
• Genre-specific optimization
• Multi-scale tempogram analysis
• Hybrid approach (autocorr → tempogram)
• Real audio processing

Example

import aubio

# Basic autocorrelation with optimizations
tempo = aubio.tempo(buf_size=1024, hop_size=256, samplerate=44100)
tempo.set_multi_octave(1)

# Multi-scale tempogram for analysis
tempo.set_use_tempogram(1)
tempo.set_onset_enhancement(1)
tempo.set_multiscale_tempogram(1)

# Genre-specific optimization
tempo.set_tempo_prior_mean(128.0)  # EDM: tight range around 128 BPM
tempo.set_tempo_prior_std(0.5)

Test Results

• C tests: 66/66 passing (unchanged)
• Python tests: 44/44 passing (19 original + 25 new)
• Total: 110 tests
• Benchmark performance validated against TEMPO_WORK_SUMMARY.md claims

All Python bindings auto-generate from C headers - no manual binding code required.

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Original prompt

Review TEMPO_WORK_SUMMARY.md

Identify any core issues or changes that need resolving prior to merging this PR

Pay particular attention to testing and the test harnesses - they must be solid prior to merging and use both synthetic and audio file based testing and benchmarking

Implement any outstanding changes and ensure that the improved beat tracking is available in python bindings

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