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librosa.power_to_db

librosa.power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0)[source]

Convert a power spectrogram (amplitude squared) to decibel (dB) units

This computes the scaling 10 * log10(S / ref) in a numerically stable way.

Parameters:

Snp.ndarray

input power

refscalar or callable

If scalar, the amplitude abs(S) is scaled relative to ref:

10 * log10(S / ref)

Zeros in the output correspond to positions where S == ref.

If callable, the reference value is computed as ref(S).

aminfloat > 0 [scalar]

minimum threshold for abs(S) and ref

top_dbfloat >= 0 [scalar]

threshold the output at top_db below the peak: max(10 * log10(S)) - top_db

Returns:

S_dbnp.ndarray: S_db ~= 10 * log10(S) - 10 * log10(ref)

See also

perceptual_weighting
db_to_power
amplitude_to_db
db_to_amplitude

Notes

This function caches at level 30.

Examples

Get a power spectrogram from a waveform y

>>> y, sr = librosa.load(librosa.ex('trumpet'))
>>> S = np.abs(librosa.stft(y))
>>> librosa.power_to_db(S**2)
array([[-41.809, -41.809, ..., -41.809, -41.809],
       [-41.809, -41.809, ..., -41.809, -41.809],
       ...,
       [-41.809, -41.809, ..., -41.809, -41.809],
       [-41.809, -41.809, ..., -41.809, -41.809]], dtype=float32)

Compute dB relative to peak power

>>> librosa.power_to_db(S**2, ref=np.max)
array([[-80., -80., ..., -80., -80.],
       [-80., -80., ..., -80., -80.],
       ...,
       [-80., -80., ..., -80., -80.],
       [-80., -80., ..., -80., -80.]], dtype=float32)

Or compare to median power

>>> librosa.power_to_db(S**2, ref=np.median)
array([[16.578, 16.578, ..., 16.578, 16.578],
       [16.578, 16.578, ..., 16.578, 16.578],
       ...,
       [16.578, 16.578, ..., 16.578, 16.578],
       [16.578, 16.578, ..., 16.578, 16.578]], dtype=float32)

And plot the results

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True)
>>> imgpow = librosa.display.specshow(S**2, sr=sr, y_axis='log', x_axis='time',
...                                   ax=ax[0])
>>> ax[0].set(title='Power spectrogram')
>>> ax[0].label_outer()
>>> imgdb = librosa.display.specshow(librosa.power_to_db(S**2, ref=np.max),
...                                  sr=sr, y_axis='log', x_axis='time', ax=ax[1])
>>> ax[1].set(title='Log-Power spectrogram')
>>> fig.colorbar(imgpow, ax=ax[0])
>>> fig.colorbar(imgdb, ax=ax[1], format="%+2.0f dB")