Analyzer Input Form
Example Data Table
| Example | Sample Rate | FFT Size | Dominant Frequency | Peak Amplitude | Noise Floor | Estimated Bandwidth |
|---|---|---|---|---|---|---|
| Voice Analysis | 44,100 Hz | 2,048 | 220 Hz | 0.70 | -68 dB | 55 Hz |
| Kick Drum Peak | 48,000 Hz | 4,096 | 72 Hz | 0.88 | -60 dB | 18 Hz |
| Synth Lead | 96,000 Hz | 8,192 | 1,200 Hz | 0.81 | -74 dB | 100 Hz |
Formula Used
Core Analyzer Equations
Bin Resolution = Sample Rate / FFT Size
Nyquist Frequency = Sample Rate / 2
Time Window = FFT Size / Sample Rate
Period = 1 / Dominant Frequency
Exact Bin Index = Dominant Frequency / Bin Resolution
Nearest Bin Center = round(Exact Bin Index) × Bin Resolution
Detuning = Dominant Frequency − Nearest Bin Center
Amplitude and Noise Equations
Corrected Peak = Peak Amplitude / Coherent Gain
RMS = Peak Amplitude / √2
Amplitude Level = 20 × log10(Corrected Peak)
SNR = Amplitude Level − Noise Floor
Headroom = 20 × log10(1 / Peak Amplitude)
Signal Energy = RMS² × Duration
Bandwidth and Distortion Equations
Bandwidth by Q = Dominant Frequency / Q Factor
Estimated Bandwidth = max(Bandwidth by Q, Bin Resolution × Mainlobe Bins)
Cutoff Range = Dominant Frequency ± (Bandwidth / 2)
THD = √(sum of squared harmonic amplitudes from 2 to n) / Fundamental × 100
Equivalent Noise Bandwidth = Bin Resolution × ENBW Multiplier
How to Use This Calculator
- Enter the sample rate used for the signal or session.
- Provide the FFT size you want to inspect.
- Enter the dominant frequency of the signal peak.
- Type the signal peak amplitude as a linear value.
- Add duration, harmonic count, decay ratio, and noise floor.
- Choose a window profile that matches your analysis style.
- Enter Q factor and sideband offset for bandwidth behavior.
- Press Analyze Signal to show results above the form.
- Review the table, inspect the plot, and export data if needed.
About This Logic Pro X Analyzer Calculator
What this tool measures
This calculator turns audio analyzer ideas into clear maths. It estimates spectral detail from simple inputs. You can inspect FFT resolution, bin alignment, bandwidth, and harmonic behavior. It also estimates RMS, SNR, THD, energy, and leakage risk. These values help explain why a peak looks stable, wide, sharp, or noisy inside a frequency analyzer.
Why FFT settings matter
FFT size changes resolution. A larger FFT creates smaller bins. Smaller bins improve frequency precision. They also increase the time window. That makes analysis slower but more detailed. Sample rate changes the Nyquist limit. It also changes the spacing of bins. Window type matters too. Each window changes coherent gain, mainlobe width, and equivalent noise bandwidth.
How the maths connects to analyzer reading
Dominant frequency maps to a bin index. If that frequency falls between bin centers, leakage increases. The detuning value shows that mismatch. A low mismatch usually looks cleaner. A larger mismatch spreads energy across neighboring bins. Q factor estimates how tight the main peak should be. Harmonic count and decay ratio estimate added peaks above the fundamental.
Why exports and charts help
The result table is useful for documentation. CSV export works well for spreadsheets and reporting. PDF export is helpful for sharing. The chart gives a quick spectrum view. It shows the dominant peak, sidebands, and harmonic structure. This makes the calculator useful for learning, planning, testing, and explaining analyzer behaviour with direct numbers.
FAQs
1. What does this calculator actually analyze?
It estimates analyzer metrics from user inputs. It does not read audio files. It models FFT resolution, peak placement, bandwidth, distortion, and noise using common signal analysis equations.
2. Why is FFT size important?
FFT size controls frequency resolution. Larger FFT values create narrower bins. That improves detail in frequency estimation, but it also increases the time window used for the analysis.
3. What is bin resolution?
Bin resolution is the spacing between adjacent FFT bins. It equals sample rate divided by FFT size. Smaller spacing gives more precise frequency placement.
4. Why does window type change the result?
Windowing changes coherent gain, mainlobe width, and noise bandwidth. That means peak amplitude and spectral spread can look different even when the original signal stays the same.
5. What does leakage risk mean here?
Leakage risk shows how far the dominant frequency sits from the nearest bin center. Greater mismatch spreads energy into nearby bins and makes the peak look wider.
6. How is THD estimated?
THD is estimated from the harmonic decay pattern you enter. The calculator sums harmonic amplitudes from the second harmonic onward and compares them with the fundamental.
7. Can I use this for learning spectrum analysis?
Yes. It is useful for practice and explanation. The table, formulas, and plot help connect frequency-domain maths to the behavior seen in a typical analyzer display.
8. What do CSV and PDF exports include?
They export the calculated result table. That includes all main analyzer metrics such as bin resolution, RMS, SNR, bandwidth, THD, cutoff values, and leakage risk.