librosa.core.estimate_tuning¶

librosa.core.estimate_tuning(y=None, sr=22050, S=None, n_fft=2048, resolution=0.01, bins_per_octave=12, **kwargs)[source]¶

Estimate the tuning of an audio time series or spectrogram input.

Parameters:

y: np.ndarray [shape=(n,)] or None: audio signal
sr : number > 0 [scalar]: audio sampling rate of y
S: np.ndarray [shape=(d, t)] or None: magnitude or power spectrogram
n_fft : int > 0 [scalar] or None: number of FFT bins to use, if y is provided.
resolution : float in (0, 1): Resolution of the tuning as a fraction of a bin. 0.01 corresponds to measurements in cents.
bins_per_octave : int > 0 [scalar]: How many frequency bins per octave
kwargs : additional keyword arguments: Additional arguments passed to piptrack

Returns:

tuning: float in `[-0.5, 0.5)`: estimated tuning deviation (fractions of a bin)

See also

piptrack: Pitch tracking by parabolic interpolation

Examples

>>> # With time-series input
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> librosa.estimate_tuning(y=y, sr=sr)
0.089999999999999969

>>> # In tenths of a cent
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> librosa.estimate_tuning(y=y, sr=sr, resolution=1e-3)
0.093999999999999972

>>> # Using spectrogram input
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.stft(y))
>>> librosa.estimate_tuning(S=S, sr=sr)
0.089999999999999969

>>> # Using pass-through arguments to `librosa.piptrack`
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> librosa.estimate_tuning(y=y, sr=sr, n_fft=8192,
...                         fmax=librosa.note_to_hz('G#9'))
0.070000000000000062