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remove analyze scripts
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analyze_esp32_q_fixed.py
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analyze_esp32_q_fixed.py
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#!/usr/bin/env python3
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# Analyze effective Q factor in ESP32 approach - FIXED
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import math
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fs = 44100 # Sample rate
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N = 4096 # FFT size
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fmin = 20 # Minimum frequency
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print("ESP32 CQT Analysis (Corrected)")
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print("==============================")
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print(f"FFT size: {N}, Sample rate: {fs} Hz, Min freq: {fmin} Hz")
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print()
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# Test frequencies spanning 10 octaves
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freqs = [20, 27.5, 40, 55, 80, 110, 160, 220, 440, 880, 1760, 3520, 7040, 14080, 20000]
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print("Freq(Hz) | Factor | Window | Duration(ms) | Eff. Q | BW(Hz) | BW(cents)")
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print("---------|--------|--------|--------------|--------|--------|----------")
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for f in freqs:
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# ESP32 window calculation: N_window = N / (f/fmin)
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factor = f / fmin
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window_length = N / factor # Keep as float for accurate Q
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window_length_int = int(window_length)
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# Window duration in milliseconds
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duration_ms = window_length / fs * 1000
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# Effective Q = window_length * f / fs
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eff_q = window_length * f / fs
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# Bandwidth = f / Q
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bandwidth = f / eff_q
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# Bandwidth in cents (1 semitone = 100 cents)
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# cents = 1200 * log2(f2/f1) where f2 = f + bandwidth/2, f1 = f - bandwidth/2
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bw_cents = 1200 * math.log2((f + bandwidth/2) / (f - bandwidth/2))
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print(f"{f:7.1f} | {factor:6.1f} | {window_length_int:6d} | {duration_ms:12.1f} | "
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f"{eff_q:6.1f} | {bandwidth:6.1f} | {bw_cents:8.0f}")
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print()
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print("Key Findings:")
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print("-------------")
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print("1. ESP32 method gives CONSTANT Q ≈ 1.86 for all frequencies!")
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print("2. This is about 9x lower than ideal Q ≈ 17 for 12 bins/octave")
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print("3. Bandwidth is constant at ~7.5 semitones (should be ~0.83 semitones)")
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print()
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print("Trade-offs:")
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print("- PRO: All windows fit in FFT size, simple implementation")
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print("- PRO: No truncation artifacts")
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print("- CON: Poor frequency resolution (9x worse than ideal)")
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print("- CON: Can't distinguish adjacent notes (bandwidth > 1 semitone)")
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print()
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print("For electronic music with 20Hz start:")
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print(f"- Window at 20Hz: {N/1:.0f} samples = {N/1/fs*1000:.1f}ms")
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print(f"- Window at 20kHz: {N/1000:.0f} samples = {N/1000/fs*1000:.1f}ms")
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fft_compute_analysis.py
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fft_compute_analysis.py
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#!/usr/bin/env python3
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# Analyze computational cost of different FFT sizes
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import math
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print("FFT Computational Cost Analysis")
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print("===============================")
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print()
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# FFT complexity is O(N log N)
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sizes = [4096, 8192, 16384, 32768, 65536]
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base_size = 4096
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print("FFT Size | Ops (N log N) | Relative Cost | Time @ 1GHz")
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print("---------|---------------|---------------|-------------")
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for N in sizes:
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ops = N * math.log2(N)
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relative = ops / (base_size * math.log2(base_size))
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# Assume 10 clock cycles per complex multiply-add
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# Modern CPUs can do ~1 operation per clock with SIMD
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time_ms = (ops * 10) / (1e9) * 1000 # milliseconds at 1GHz
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print(f"{N:7d} | {ops:13.0f} | {relative:13.1f}x | {time_ms:10.2f}ms")
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print()
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print("Real-world estimates (with KISS FFT, no SIMD):")
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print("- 4096-point: ~1-2ms on modern CPU")
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print("- 16384-point: ~5-10ms")
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print("- 32768-point: ~12-24ms")
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print()
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print("For 60 FPS: 16.7ms per frame total")
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print("For 30 FPS: 33.3ms per frame total")
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print()
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# Memory usage
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print("Memory Requirements:")
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print("Size | Samples | Real FFT Output | Kernels (120 bins)")
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print("------|---------|-----------------|-------------------")
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for N in sizes:
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samples = N * 4 # float32
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fft_out = (N//2 + 1) * 8 # complex float32
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# Assume 30% sparsity for kernels
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kernels = 120 * (N//2 + 1) * 8 * 0.3
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total = (samples + fft_out + kernels) / 1024
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print(f"{N:5d} | {samples/1024:7.0f}K | {fft_out/1024:15.0f}K | {kernels/1024:17.0f}K (Total: {total:.0f}K)")