Pitch Detection

Two methods for estimating the fundamental frequency (F0) of a signal.

Autocorrelation method

Searches for the strongest peak in the normalised autocorrelation:

\[f_0 = \frac{f_s}{\tau_\text{peak}}\]

More robust for noisy or strongly harmonic signals.

import pyminidsp as md

signal = md.sine_wave(4096, freq=200.0, sample_rate=16000.0)
f0 = md.f0_autocorrelation(signal, sample_rate=16000.0,
                             min_freq_hz=80.0, max_freq_hz=400.0)
print(f"Estimated F0: {f0:.1f} Hz")  # ≈ 200.0

FFT peak-picking method

Applies a Hann window, computes the magnitude spectrum, and identifies the dominant peak in the requested frequency range:

\[f_0 = \frac{k_\text{peak} \cdot f_s}{N}\]

Simple and fast, but can lock onto harmonics (2f0, 3f0) when the fundamental is weak.

f0 = md.f0_fft(signal, sample_rate=16000.0,
                min_freq_hz=80.0, max_freq_hz=400.0)
print(f"Estimated F0: {f0:.1f} Hz")  # ≈ 200.0

Practical notes

• Search range is critical for both methods. Use prior knowledge of the expected pitch range (e.g. 80–400 Hz for speech).

• A return value of 0.0 means no reliable F0 was found — typically silence, unvoiced speech, or noisy frames.

• Longer frames improve resolution but reduce time accuracy.

md.shutdown()