librosa.power_to_db#

librosa.power_to_db(S, *, ref=1.0, amin=1e-10, top_db=80.0, axes='auto')[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, axis=axes, keepdims=True).

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/ref)) - top_db For multi-channel inputs, with axes=’auto’, peaks are calculated independently for each channel.

axesNone, “auto”, int, or tuple of int

Axis or axes along which to compute the reference value (if ref is callable). If auto, then axes will be inferred as the trailing dimensions of S:

If S is scalar, then axes=None

If S is 1D, then axes=(-1,)

If S is >=2D, then axes=(-2, -1)

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.loadx('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])
>>> librosa.display.colorbar_db(imgdb, ax=ax[1])