Caution

You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.10.2.

librosa.feature.chroma_stft

librosa.feature.chroma_stft(*, y=None, sr=22050, S=None, norm=inf, n_fft=2048, hop_length=512, win_length=None, window='hann', center=True, pad_mode='constant', tuning=None, n_chroma=12, **kwargs)

Compute a chromagram from a waveform or power spectrogram.

This implementation is derived from chromagram_E [1]

[1]

Ellis, Daniel P.W. “Chroma feature analysis and synthesis” 2007/04/21 http://labrosa.ee.columbia.edu/matlab/chroma-ansyn/

Parameters:

ynp.ndarray [shape=(…, n)] or None

audio time series. Multi-channel is supported.

srnumber > 0 [scalar]

sampling rate of y

Snp.ndarray [shape=(…, d, t)] or None

power spectrogram

normfloat or None

Column-wise normalization. See librosa.util.normalize for details.

If None, no normalization is performed.

n_fftint > 0 [scalar]

FFT window size if provided y, sr instead of S

hop_lengthint > 0 [scalar]

hop length if provided y, sr instead of S

win_lengthint <= n_fft [scalar]

Each frame of audio is windowed by window(). The window will be of length win_length and then padded with zeros to match n_fft.

If unspecified, defaults to win_length = n_fft.

windowstring, tuple, number, function, or np.ndarray [shape=(n_fft,)]

• a window specification (string, tuple, or number); see scipy.signal.get_window

• a window function, such as scipy.signal.windows.hann

• a vector or array of length n_fft

centerboolean

• If True, the signal y is padded so that frame t is centered at y[t * hop_length].

• If False, then frame t begins at y[t * hop_length]

pad_modestring

If center=True, the padding mode to use at the edges of the signal. By default, STFT uses zero padding.

tuningfloat [scalar] or None.

Deviation from A440 tuning in fractional chroma bins. If None, it is automatically estimated.

n_chromaint > 0 [scalar]

Number of chroma bins to produce (12 by default).

**kwargsadditional keyword arguments

Arguments to parameterize chroma filters. See librosa.filters.chroma for details.

Returns:

chromagramnp.ndarray [shape=(…, n_chroma, t)]

Normalized energy for each chroma bin at each frame.

See also

librosa.filters.chroma

Chroma filter bank construction

librosa.util.normalize

Vector normalization

Examples

>>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=15)
>>> librosa.feature.chroma_stft(y=y, sr=sr)
array([[1.   , 0.962, ..., 0.143, 0.278],
       [0.688, 0.745, ..., 0.103, 0.162],
       ...,
       [0.468, 0.598, ..., 0.18 , 0.342],
       [0.681, 0.702, ..., 0.553, 1.   ]], dtype=float32)

Use an energy (magnitude) spectrum instead of power spectrogram

>>> S = np.abs(librosa.stft(y))
>>> chroma = librosa.feature.chroma_stft(S=S, sr=sr)
>>> chroma
array([[1.   , 0.973, ..., 0.527, 0.569],
       [0.774, 0.81 , ..., 0.518, 0.506],
       ...,
       [0.624, 0.73 , ..., 0.611, 0.644],
       [0.766, 0.822, ..., 0.92 , 1.   ]], dtype=float32)

Use a pre-computed power spectrogram with a larger frame

>>> S = np.abs(librosa.stft(y, n_fft=4096))**2
>>> chroma = librosa.feature.chroma_stft(S=S, sr=sr)
>>> chroma
array([[0.994, 0.873, ..., 0.169, 0.227],
       [0.735, 0.64 , ..., 0.141, 0.135],
       ...,
       [0.6  , 0.937, ..., 0.214, 0.257],
       [0.743, 0.937, ..., 0.684, 0.815]], dtype=float32)

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> img = librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
...                                y_axis='log', x_axis='time', ax=ax[0])
>>> fig.colorbar(img, ax=[ax[0]])
>>> ax[0].label_outer()
>>> img = librosa.display.specshow(chroma, y_axis='chroma', x_axis='time', ax=ax[1])
>>> fig.colorbar(img, ax=[ax[1]])