librosa.display.specshow(data, x_coords=None, y_coords=None, x_axis=None, y_axis=None, sr=22050, hop_length=512, fmin=None, fmax=None, tuning=0.0, bins_per_octave=12, key='C:maj', Sa=None, mela=None, thaat=None, ax=None, **kwargs)[source]

Display a spectrogram/chromagram/cqt/etc.

For a detailed overview of this function, see Using display.specshow

datanp.ndarray [shape=(d, n)]

Matrix to display (e.g., spectrogram)

srnumber > 0 [scalar]

Sample rate used to determine time scale in x-axis.

hop_lengthint > 0 [scalar]

Hop length, also used to determine time scale in x-axis

x_axis, y_axisNone or str

Range for the x- and y-axes.

Valid types are:

  • None, ‘none’, or ‘off’ : no axis decoration is displayed.

Frequency types:

  • ‘linear’, ‘fft’, ‘hz’ : frequency range is determined by the FFT window and sampling rate.

  • ‘log’ : the spectrum is displayed on a log scale.

  • ‘mel’ : frequencies are determined by the mel scale.

  • ‘cqt_hz’ : frequencies are determined by the CQT scale.

  • ‘cqt_note’ : pitches are determined by the CQT scale.

  • cqt_svara : like cqt_note but using Hindustani or Carnatic svara

All frequency types are plotted in units of Hz.

Any spectrogram parameters (hop_length, sr, bins_per_octave, etc.) used to generate the input data should also be provided when calling specshow.

Categorical types:

  • ‘chroma’ : pitches are determined by the chroma filters. Pitch classes are arranged at integer locations (0-11) according to a given key.

  • chroma_h, chroma_c: pitches are determined by chroma filters, and labeled as svara in the Hindustani (chroma_h) or Carnatic (chroma_c) according to a given thaat (Hindustani) or melakarta raga (Carnatic).

  • ‘tonnetz’ : axes are labeled by Tonnetz dimensions (0-5)

  • ‘frames’ : markers are shown as frame counts.

Time types:

  • ‘time’markers are shown as milliseconds, seconds, minutes, or hours.

    Values are plotted in units of seconds.

  • ‘s’ : markers are shown as seconds.

  • ‘ms’ : markers are shown as milliseconds.

  • ‘lag’ : like time, but past the halfway point counts as negative values.

  • ‘lag_s’ : same as lag, but in seconds.

  • ‘lag_ms’ : same as lag, but in milliseconds.


  • ‘tempo’markers are shown as beats-per-minute (BPM)

    using a logarithmic scale. This is useful for visualizing the outputs of feature.tempogram.

  • ‘fourier_tempo’same as ‘tempo’, but used when

    tempograms are calculated in the Frequency domain using feature.fourier_tempogram.

x_coords, y_coordsnp.ndarray [shape=data.shape[0 or 1]+1]

Optional positioning coordinates of the input data. These can be use to explicitly set the location of each element data[i, j], e.g., for displaying beat-synchronous features in natural time coordinates.

If not provided, they are inferred from x_axis and y_axis.

fminfloat > 0 [scalar] or None

Frequency of the lowest spectrogram bin. Used for Mel and CQT scales.

If y_axis is cqt_hz or cqt_note and fmin is not given, it is set by default to note_to_hz('C1').

fmaxfloat > 0 [scalar] or None

Used for setting the Mel frequency scales


Tuning deviation from A440, in fractions of a bin.

This is used for CQT frequency scales, so that fmin is adjusted to fmin * 2**(tuning / bins_per_octave).

bins_per_octaveint > 0 [scalar]

Number of bins per octave. Used for CQT frequency scale.


The reference key to use when using note axes (cqt_note, chroma).

Safloat or int

If using Hindustani or Carnatic svara axis decorations, specify Sa.

For cqt_svara, Sa should be specified as a frequency in Hz.

For chroma_c or chroma_h, Sa should correspond to the position of Sa within the chromagram. If not provided, Sa will default to 0 (equivalent to C)

melastr or int, optional

If using chroma_c or cqt_svara display mode, specify the melakarta raga.

thaatstr, optional

If using chroma_h display mode, specify the parent thaat.

axmatplotlib.axes.Axes or None

Axes to plot on instead of the default plt.gca().

kwargsadditional keyword arguments

Arguments passed through to matplotlib.pyplot.pcolormesh.

By default, the following options are set:

  • rasterized=True

  • shading='flat'

  • edgecolors='None'


The color mesh object produced by matplotlib.pyplot.pcolormesh

See also


Automatic colormap detection



Visualize an STFT power spectrum using default parameters

>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.ex('choice'), duration=15)
>>> fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
>>> D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)
>>> img = librosa.display.specshow(D, y_axis='linear', x_axis='time',
...                                sr=sr, ax=ax[0])
>>> ax[0].set(title='Linear-frequency power spectrogram')
>>> ax[0].label_outer()

Or on a logarithmic scale, and using a larger hop

>>> hop_length = 1024
>>> D = librosa.amplitude_to_db(np.abs(librosa.stft(y, hop_length=hop_length)),
...                             ref=np.max)
>>> librosa.display.specshow(D, y_axis='log', sr=sr, hop_length=hop_length,
...                          x_axis='time', ax=ax[1])
>>> ax[1].set(title='Log-frequency power spectrogram')
>>> ax[1].label_outer()
>>> fig.colorbar(img, ax=ax, format="%+2.f dB")

(Source code)