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librosa.core.power_to_db

librosa.core.power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0)

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.util.example_audio_file())
>>> S = np.abs(librosa.stft(y))
>>> librosa.power_to_db(S**2)
array([[-33.293, -27.32 , ..., -33.293, -33.293],
       [-33.293, -25.723, ..., -33.293, -33.293],
       ...,
       [-33.293, -33.293, ..., -33.293, -33.293],
       [-33.293, -33.293, ..., -33.293, -33.293]], dtype=float32)

Compute dB relative to peak power

>>> librosa.power_to_db(S**2, ref=np.max)
array([[-80.   , -74.027, ..., -80.   , -80.   ],
       [-80.   , -72.431, ..., -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([[-0.189,  5.784, ..., -0.189, -0.189],
       [-0.189,  7.381, ..., -0.189, -0.189],
       ...,
       [-0.189, -0.189, ..., -0.189, -0.189],
       [-0.189, -0.189, ..., -0.189, -0.189]], dtype=float32)

And plot the results

>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.subplot(2, 1, 1)
>>> librosa.display.specshow(S**2, sr=sr, y_axis='log')
>>> plt.colorbar()
>>> plt.title('Power spectrogram')
>>> plt.subplot(2, 1, 2)
>>> librosa.display.specshow(librosa.power_to_db(S**2, ref=np.max),
...                          sr=sr, y_axis='log', x_axis='time')
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.title('Log-Power spectrogram')
>>> plt.tight_layout()
>>> plt.show()