librosa.feature.spectral_rolloff(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, win_length=None, window='hann', center=True, pad_mode='reflect', freq=None, roll_percent=0.85)[source]

Compute roll-off frequency.

The roll-off frequency is defined for each frame as the center frequency for a spectrogram bin such that at least roll_percent (0.85 by default) of the energy of the spectrum in this frame is contained in this bin and the bins below. This can be used to, e.g., approximate the maximum (or minimum) frequency by setting roll_percent to a value close to 1 (or 0).

ynp.ndarray [shape=(n,)] or None

audio time series

srnumber > 0 [scalar]

audio sampling rate of y

Snp.ndarray [shape=(d, t)] or None

(optional) spectrogram magnitude

n_fftint > 0 [scalar]

FFT window size

hop_lengthint > 0 [scalar]

hop length for STFT. See librosa.stft for details.

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,)]
  • 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]


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

freqNone or np.ndarray [shape=(d,) or shape=(d, t)]

Center frequencies for spectrogram bins. If None, then FFT bin center frequencies are used. Otherwise, it can be a single array of d center frequencies,


freq is assumed to be sorted in increasing order

roll_percentfloat [0 < roll_percent < 1]

Roll-off percentage.

rolloffnp.ndarray [shape=(1, t)]

roll-off frequency for each frame


From time-series input

>>> y, sr = librosa.load(librosa.ex('trumpet'))
>>> # Approximate maximum frequencies with roll_percent=0.85 (default)
>>> librosa.feature.spectral_rolloff(y=y, sr=sr)
array([[2583.984, 3036.182, ..., 9173.145, 9248.511]])
>>> # Approximate maximum frequencies with roll_percent=0.99
>>> rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.99)
>>> rolloff
array([[ 7192.09 ,  6739.893, ..., 10960.4  , 10992.7  ]])
>>> # Approximate minimum frequencies with roll_percent=0.01
>>> rolloff_min = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.01)
>>> rolloff_min
array([[516.797, 538.33 , ..., 764.429, 764.429]])

From spectrogram input

>>> S, phase = librosa.magphase(librosa.stft(y))
>>> librosa.feature.spectral_rolloff(S=S, sr=sr)
array([[2583.984, 3036.182, ..., 9173.145, 9248.511]])
>>> # With a higher roll percentage:
>>> librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.95)
array([[ 3919.043,  3994.409, ..., 10443.604, 10594.336]])
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
...                          y_axis='log', x_axis='time', ax=ax)
>>> ax.plot(librosa.times_like(rolloff), rolloff[0], label='Roll-off frequency (0.99)')
>>> ax.plot(librosa.times_like(rolloff), rolloff_min[0], color='w',
...         label='Roll-off frequency (0.01)')
>>> ax.legend(loc='lower right')
>>> ax.set(title='log Power spectrogram')

(Source code)