Caution

You're reading the documentation for a development version. For the latest released version, please have a look at 0.9.1.

librosa.feature.stack_memory

librosa.feature.stack_memory(data, *, n_steps=2, delay=1, **kwargs)

Short-term history embedding: vertically concatenate a data vector or matrix with delayed copies of itself.

Each column data[:, i] is mapped to:

data[..., i] ->  [data[..., i],
                  data[..., i - delay],
                  ...
                  data[..., i - (n_steps-1)*delay]]

For columns i < (n_steps - 1) * delay, the data will be padded. By default, the data is padded with zeros, but this behavior can be overridden by supplying additional keyword arguments which are passed to np.pad().

Parameters

datanp.ndarray [shape=(…, d, t)]

Input data matrix. If data is a vector (data.ndim == 1), it will be interpreted as a row matrix and reshaped to (1, t).

n_stepsint > 0 [scalar]

embedding dimension, the number of steps back in time to stack

delayint != 0 [scalar]

the number of columns to step.

Positive values embed from the past (previous columns).

Negative values embed from the future (subsequent columns).

**kwargsadditional keyword arguments

Additional arguments to pass to numpy.pad

Returns

data_historynp.ndarray [shape=(…, m * d, t)]

data augmented with lagged copies of itself, where m == n_steps - 1.

Notes

This function caches at level 40.

Examples

Keep two steps (current and previous)

>>> data = np.arange(-3, 3)
>>> librosa.feature.stack_memory(data)
array([[-3, -2, -1,  0,  1,  2],
       [ 0, -3, -2, -1,  0,  1]])

Or three steps

>>> librosa.feature.stack_memory(data, n_steps=3)
array([[-3, -2, -1,  0,  1,  2],
       [ 0, -3, -2, -1,  0,  1],
       [ 0,  0, -3, -2, -1,  0]])

Use reflection padding instead of zero-padding

>>> librosa.feature.stack_memory(data, n_steps=3, mode='reflect')
array([[-3, -2, -1,  0,  1,  2],
       [-2, -3, -2, -1,  0,  1],
       [-1, -2, -3, -2, -1,  0]])

Or pad with edge-values, and delay by 2

>>> librosa.feature.stack_memory(data, n_steps=3, delay=2, mode='edge')
array([[-3, -2, -1,  0,  1,  2],
       [-3, -3, -3, -2, -1,  0],
       [-3, -3, -3, -3, -3, -2]])

Stack time-lagged beat-synchronous chroma edge padding

>>> y, sr = librosa.load(librosa.ex('sweetwaltz'), duration=10)
>>> chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
>>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr, hop_length=512)
>>> beats = librosa.util.fix_frames(beats, x_min=0)
>>> chroma_sync = librosa.util.sync(chroma, beats)
>>> chroma_lag = librosa.feature.stack_memory(chroma_sync, n_steps=3,
...                                           mode='edge')

Plot the result

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> beat_times = librosa.frames_to_time(beats, sr=sr, hop_length=512)
>>> librosa.display.specshow(chroma_lag, y_axis='chroma', x_axis='time',
...                          x_coords=beat_times, ax=ax)
>>> ax.text(1.0, 1/6, "Lag=0", transform=ax.transAxes, rotation=-90, ha="left", va="center")
>>> ax.text(1.0, 3/6, "Lag=1", transform=ax.transAxes, rotation=-90, ha="left", va="center")
>>> ax.text(1.0, 5/6, "Lag=2", transform=ax.transAxes, rotation=-90, ha="left", va="center")
>>> ax.set(title='Time-lagged chroma', ylabel="")