Core functionality

Core resampling interface

resampy.core.resample(x, sr_orig, sr_new, axis=-1, filter='kaiser_best', parallel=False, **kwargs)

Resample a signal x from sr_orig to sr_new along a given axis.

Parameters:

xnp.ndarray, dtype=np.float*

The input signal(s) to resample.

sr_origint > 0

The sampling rate of x

sr_newint > 0

The target sampling rate of the output signal(s)

If sr_new == sr_orig, then a copy of x is returned with no interpolation performed.

axisint

The target axis along which to resample x

filteroptional, str or callable

The resampling filter to use.

By default, uses the kaiser_best (pre-computed filter).

paralleloptional, bool

Enable/disable parallel computation exploiting multi-threading.

Default: False.

**kwargs

additional keyword arguments provided to the specified filter

Returns:

ynp.ndarray

x resampled to sr_new

Raises:

ValueError

if sr_orig or sr_new is not positive

TypeError

if the input signal x has an unsupported data type.

Examples

>>> import resampy
>>> np.set_printoptions(precision=3, suppress=True)
>>> # Generate a sine wave at 440 Hz for 5 seconds
>>> sr_orig = 44100.0
>>> x = np.sin(2 * np.pi * 440.0 / sr_orig * np.arange(5 * sr_orig))
>>> x
array([ 0.   ,  0.063, ..., -0.125, -0.063])
>>> # Resample to 22050 with default parameters
>>> resampy.resample(x, sr_orig, 22050)
array([ 0.011,  0.122,  0.25 , ..., -0.366, -0.25 , -0.122])
>>> # Resample using the fast (low-quality) filter
>>> resampy.resample(x, sr_orig, 22050, filter='kaiser_fast')
array([ 0.012,  0.121,  0.251, ..., -0.365, -0.251, -0.121])
>>> # Resample using a high-quality filter
>>> resampy.resample(x, sr_orig, 22050, filter='kaiser_best')
array([ 0.011,  0.122,  0.25 , ..., -0.366, -0.25 , -0.122])
>>> # Resample using a Hann-windowed sinc filter
>>> import scipy.signal
>>> resampy.resample(x, sr_orig, 22050, filter='sinc_window',
...                  window=scipy.signal.windows.hann)
array([ 0.011,  0.123,  0.25 , ..., -0.366, -0.25 , -0.123])

>>> # Generate stereo data
>>> x_right = np.sin(2 * np.pi * 880.0 / sr_orig * np.arange(len(x)))
>>> x_stereo = np.stack([x, x_right])
>>> x_stereo.shape
(2, 220500)
>>> # Resample along the time axis (1)
>>> y_stereo = resampy.resample(x_stereo, sr_orig, 22050, axis=1)
>>> y_stereo.shape
(2, 110250)

resampy.core.resample_nu(x, sr_orig, t_out, axis=-1, filter='kaiser_best', parallel=False, **kwargs)

Interpolate a signal x at specified positions (t_out) along a given axis.

Parameters:

xnp.ndarray, dtype=np.float*

The input signal(s) to resample.

sr_origfloat

Sampling rate of the input signal (x).

t_outnp.ndarray, dtype=np.float*

Position of the output samples.

axisint

The target axis along which to resample x

filteroptional, str or callable

The resampling filter to use.

By default, uses the kaiser_best (pre-computed filter).

paralleloptional, bool

Enable/disable parallel computation exploiting multi-threading.

Default: True.

**kwargs

additional keyword arguments provided to the specified filter

Returns:

ynp.ndarray

x resampled to t_out

Raises:

TypeError

if the input signal x has an unsupported data type.

Notes

Differently form the resample function the filter rolloff is not automatically adapted in case of subsampling. For this reason results obtained with the resample_nu could be slightly different form the ones obtained with resample if the filter parameters are not carefully set by the user.

Examples

>>> import resampy
>>> np.set_printoptions(precision=3, suppress=True)
>>> # Generate a sine wave at 100 Hz for 5 seconds
>>> sr_orig = 100.0
>>> f0 = 1
>>> t = np.arange(5 * sr_orig) / sr_orig
>>> x = np.sin(2 * np.pi * f0 * t)
>>> x
array([ 0.   ,  0.063,  0.125, ..., -0.187, -0.125, -0.063])
>>> # Resample to non-uniform sampling
>>> t_new = np.log2(1 + t)[::5] - t[0]
>>> resampy.resample_nu(x, sr_orig, t_new)
array([ 0.001,  0.427,  0.76 , ..., -0.3  , -0.372, -0.442])

Filters

Filter construction and loading.

resampy provides two pre-computed resampling filters which are tuned for either high-quality or fast calculation. These filters are constructed by the create_filters.py script.

• kaiser_best :

  > Parameters for kaiser_best: > —————————————- > beta = 12.9846 > roll = 0.917347 > # zeros = 50 > precision = 13 > attenuation = -120.0 > —————————————-

• kaiser_fast :

  > Parameters for kaiser_fast: > —————————————- > beta = 9.90322 > roll = 0.868212 > # zeros = 24 > precision = 9 > attenuation = -93.0 > —————————————-

These filters can be used by calling resample as follows:

>>> # High-quality
>>> resampy.resample(x, sr_orig, sr_new, filter='kaiser_best')  
>>> # Fast calculation
>>> resampy.resample(x, sr_orig, sr_new, filter='kaiser_fast')  

It is also possible to construct custom filters as follows:

>>> resampy.resample(x, sr_orig, sr_new, filter='sinc_window',
...                  **kwargs)                                  

where **kwargs are additional parameters to sinc_window.

resampy.filters.clear_cache()

Clear the filter cache.

Calling this function will ensure that packaged filters are reloaded upon the next usage.

resampy.filters.get_filter(name_or_function, **kwargs)

Retrieve a window given its name or function handle.

Parameters:

name_or_functionstr or callable

If a function, returns name_or_function(**kwargs).

If a string, and it matches the name of one of the defined filter functions, the corresponding function is called with **kwargs.

If a string, and it matches the name of a pre-computed filter, the corresponding filter is retrieved, and kwargs is ignored.

Valid pre-computed filter names are:

• ‘kaiser_fast’

• ‘kaiser_best’

**kwargs

Additional keyword arguments passed to name_or_function (if callable)

Returns:

half_windownp.ndarray

The right wing of the interpolation filter

precisionint > 0

The number of samples between zero-crossings of the filter

rollofffloat > 0

The roll-off frequency of the filter as a fraction of Nyquist

Raises:

NotImplementedError

If name_or_function cannot be found as a filter.

resampy.filters.sinc_window(num_zeros=64, precision=9, window=None, rolloff=0.945)

Construct a windowed sinc interpolation filter

Parameters:

num_zerosint > 0

The number of zero-crossings to retain in the sinc filter

precisionint > 0

The number of filter coefficients to retain for each zero-crossing

windowcallable

The window function. By default, uses a Hann window.

rollofffloat > 0

The roll-off frequency (as a fraction of nyquist)

Returns:

interp_window: np.ndarray [shape=(num_zeros * num_table + 1)]

The interpolation window (right-hand side)

num_bits: int

The number of bits of precision to use in the filter table

rollofffloat > 0

The roll-off frequency of the filter, as a fraction of Nyquist

Raises:

TypeError

if window is not callable or None

ValueError

if num_zeros < 1, precision < 1, or rolloff is outside the range (0, 1].

Examples

>>> import scipy, scipy.signal
>>> import resampy
>>> np.set_printoptions(threshold=5, suppress=False)
>>> # A filter with 10 zero-crossings, 32 samples per crossing, and a
>>> # Hann window for tapering.
>>> halfwin, prec, rolloff = resampy.filters.sinc_window(num_zeros=10, precision=5,
...                                                      window=scipy.signal.windows.hann)
>>> halfwin
array([  9.450e-01,   9.436e-01, ...,  -7.455e-07,  -0.000e+00])
>>> prec
32
>>> rolloff
0.945

>>> # Or using sinc-window filter construction directly in resample
>>> y = resampy.resample(x, sr_orig, sr_new, filter='sinc_window',
...                      num_zeros=10, precision=5,
...                      window=scipy.signal.windows.hann)