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Source code for librosa.feature.utils

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Feature manipulation utilities"""

from warnings import warn
import numpy as np
import scipy.signal

from .._cache import cache
from ..util.exceptions import ParameterError
__all__ = ['delta', 'stack_memory']


[docs]@cache(level=40) def delta(data, width=9, order=1, axis=-1, mode='interp', **kwargs): r'''Compute delta features: local estimate of the derivative of the input data along the selected axis. Delta features are computed Savitsky-Golay filtering. Parameters ---------- data : np.ndarray the input data matrix (eg, spectrogram) width : int, positive, odd [scalar] Number of frames over which to compute the delta features. Cannot exceed the length of `data` along the specified axis. If `mode='interp'`, then `width` must be at least `data.shape[axis]`. order : int > 0 [scalar] the order of the difference operator. 1 for first derivative, 2 for second, etc. axis : int [scalar] the axis along which to compute deltas. Default is -1 (columns). mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'} Padding mode for estimating differences at the boundaries. kwargs : additional keyword arguments See `scipy.signal.savgol_filter` Returns ------- delta_data : np.ndarray [shape=(d, t)] delta matrix of `data` at specified order Notes ----- This function caches at level 40. See Also -------- scipy.signal.savgol_filter Examples -------- Compute MFCC deltas, delta-deltas >>> y, sr = librosa.load(librosa.util.example_audio_file()) >>> mfcc = librosa.feature.mfcc(y=y, sr=sr) >>> mfcc_delta = librosa.feature.delta(mfcc) >>> mfcc_delta array([[ 1.666e+01, 1.666e+01, ..., 1.869e-15, 1.869e-15], [ 1.784e+01, 1.784e+01, ..., 6.085e-31, 6.085e-31], ..., [ 7.262e-01, 7.262e-01, ..., 9.259e-31, 9.259e-31], [ 6.578e-01, 6.578e-01, ..., 7.597e-31, 7.597e-31]]) >>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2) >>> mfcc_delta2 array([[ -1.703e+01, -1.703e+01, ..., 3.834e-14, 3.834e-14], [ -1.108e+01, -1.108e+01, ..., -1.068e-30, -1.068e-30], ..., [ 4.075e-01, 4.075e-01, ..., -1.565e-30, -1.565e-30], [ 1.676e-01, 1.676e-01, ..., -2.104e-30, -2.104e-30]]) >>> import matplotlib.pyplot as plt >>> plt.subplot(3, 1, 1) >>> librosa.display.specshow(mfcc) >>> plt.title('MFCC') >>> plt.colorbar() >>> plt.subplot(3, 1, 2) >>> librosa.display.specshow(mfcc_delta) >>> plt.title(r'MFCC-$\Delta$') >>> plt.colorbar() >>> plt.subplot(3, 1, 3) >>> librosa.display.specshow(mfcc_delta2, x_axis='time') >>> plt.title(r'MFCC-$\Delta^2$') >>> plt.colorbar() >>> plt.tight_layout() >>> plt.show() ''' data = np.atleast_1d(data) if mode == 'interp' and width > data.shape[axis]: raise ParameterError("when mode='interp', width={} " "cannot exceed data.shape[axis]={}".format(width, data.shape[axis])) if width < 3 or np.mod(width, 2) != 1: raise ParameterError('width must be an odd integer >= 3') if order <= 0 or not isinstance(order, int): raise ParameterError('order must be a positive integer') kwargs.pop('deriv', None) kwargs.setdefault('polyorder', order) return scipy.signal.savgol_filter(data, width, deriv=order, axis=axis, mode=mode, **kwargs)
[docs]@cache(level=40) def 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 ---------- data : np.ndarray [shape=(t,) or (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_steps : int > 0 [scalar] embedding dimension, the number of steps back in time to stack delay : int != 0 [scalar] the number of columns to step. Positive values embed from the past (previous columns). Negative values embed from the future (subsequent columns). kwargs : additional keyword arguments Additional arguments to pass to `np.pad`. Returns ------- data_history : np.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.util.example_audio_file()) >>> chroma = librosa.feature.chroma_stft(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, x_max=chroma.shape[1]) >>> 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 >>> 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) >>> plt.yticks([0, 12, 24], ['Lag=0', 'Lag=1', 'Lag=2']) >>> plt.title('Time-lagged chroma') >>> plt.colorbar() >>> plt.tight_layout() >>> plt.show() """ if n_steps < 1: raise ParameterError('n_steps must be a positive integer') if delay == 0: raise ParameterError('delay must be a non-zero integer') data = np.atleast_2d(data) t = data.shape[1] kwargs.setdefault('mode', 'constant') if kwargs['mode'] == 'constant': kwargs.setdefault('constant_values', [0]) # Pad the end with zeros, which will roll to the front below if delay > 0: padding = (int((n_steps - 1) * delay), 0) else: padding = (0, int((n_steps - 1) * -delay)) data = np.pad(data, [(0, 0), padding], **kwargs) history = data # TODO: this could be more efficient for i in range(1, n_steps): history = np.vstack([np.roll(data, -i * delay, axis=1), history]) # Trim to original width if delay > 0: history = history[:, :t] else: history = history[:, -t:] # Make contiguous return np.asfortranarray(history)