Resample

class auglib.transform.Resample(target_rate, *, preserve_level=False, bypass_prob=None, **kwargs)

Resample signal to another sampling rate.

Changes number of samples of the audio signal by applying audresample.resample() to the signal.

Parameters

• target_rate (typing.Union[int, auglib.core.observe.List]) – target rate in Hz

• preserve_level (typing.Union[bool, auglib.core.observe.Base]) – if True the root mean square value of the augmented signal will be the same as before augmentation

• bypass_prob (typing.Union[float, auglib.core.observe.Base, None]) – probability to bypass the transformation

Examples

When applying a transform on a file with auglib.Augment, it will not change its sampling rate, but its number of samples. Hence, we assume here as well that the sampling rate of the augmented signal stays at 16000 Hz.

Resample a speech signal to 8000 Hz, to speed up the signal.

>>> import audb
>>> import audiofile
>>> import audplot
>>> import auglib
>>> transform = auglib.transform.Resample(8000)
>>> files = audb.load_media("emodb", "wav/03a01Fa.wav", version="1.4.1")
>>> signal, sampling_rate = audiofile.read(files[0])
>>> augmented_signal = transform(signal, sampling_rate)
>>> audplot.waveform(augmented_signal)

Inspect its magnitude spectrum.

>>> import audmath
>>> import matplotlib.pyplot as plt
>>> import seaborn as sns
>>> sigs = [signal, augmented_signal]
>>> colors = ["#5d6370", "#e13b41"]
>>> for sig, color in zip(sigs, colors):
...     magnitude, f = plt.mlab.magnitude_spectrum(sig, Fs=sampling_rate)
...     # Smooth magnitude
...     magnitude = np.convolve(magnitude, np.ones(14) / 14, mode="same")
...     plt.plot(f, audmath.db(magnitude), color=color)
>>> plt.xlim([10, 8000])
>>> plt.ylim([-100, -45])
>>> plt.ylabel("Magnitude / dB")
>>> plt.xlabel("Frequency / Hz")
>>> plt.legend(["signal", "augmented signal"])
>>> plt.grid(alpha=0.4)
>>> sns.despine()
>>> plt.tight_layout()

Removing the upper half of the signal by frst resampling it to 8000 Hz, and then to 32000 Hz. Again, we listen to the augmented signal at its original sampling rate of 16000 Hz.

>>> transform = auglib.transform.Compose(
...     [
...         auglib.transform.Resample(8000),
...         auglib.transform.Resample(32000),
...     ],
... )
>>> augmented_signal = transform(signal, sampling_rate)
>>> audplot.waveform(augmented_signal)

Inspect its magnitude spectrum.

>>> sigs = [signal, augmented_signal]
>>> for sig, color in zip(sigs, colors):
...     magnitude, f = plt.mlab.magnitude_spectrum(sig, Fs=sampling_rate)
...     # Smooth magnitude
...     magnitude = np.convolve(magnitude, np.ones(14) / 14, mode="same")
...     plt.plot(f, audmath.db(magnitude), color=color)
>>> plt.xlim([10, 8000])
>>> plt.ylim([-100, -45])
>>> plt.ylabel("Magnitude / dB")
>>> plt.xlabel("Frequency / Hz")
>>> plt.legend(["signal", "augmented signal"])
>>> plt.grid(alpha=0.4)
>>> sns.despine()
>>> plt.tight_layout()

__call__()

Resample.__call__(signal, sampling_rate=None)

Apply transform to signal.

Parameters

• signal (numpy.ndarray) – signal to be transformed

• sampling_rate (typing.Optional[int]) – sampling rate in Hz

Return type

numpy.ndarray

Returns

augmented signal

Raises

• ValueError – if the signal shape is not support by chosen transform parameters

• ValueError – if sampling_rate is None, but the transform requires a samling rate

• RuntimeError – if the given sampling rate is incompatible with the transform

arguments

Resample.arguments

Returns arguments that are serialized.

Returns

Dictionary of arguments and their values.

Raises

RuntimeError – if arguments are found that are not assigned to attributes of the same name

Examples

>>> import audobject.testing
>>> o = audobject.testing.TestObject('test', point=(1, 1))
>>> o.arguments
{'name': 'test', 'point': (1, 1)}

borrowed_arguments

Resample.borrowed_arguments

Returns borrowed arguments.

Returns

Dictionary with borrowed arguments.

from_dict()

static Resample.from_dict(d, root=None, **kwargs)

Return type

audobject.core.object.Object

from_yaml()

static Resample.from_yaml(path_or_stream, **kwargs)

Return type

audobject.core.object.Object

from_yaml_s()

static Resample.from_yaml_s(yaml_string, **kwargs)

Return type

audobject.core.object.Object

hidden_arguments

Resample.hidden_arguments

Returns hidden arguments.

Returns

List with names of hidden arguments.

id

Resample.id

Object identifier.

The ID of an object ID is created from its non-hidden arguments.

Returns

object identifier

Examples

>>> class Foo(Object):
...    def __init__(self, bar: str):
...        self.bar = bar
>>> foo1 = Foo('I am unique!')
>>> foo1.id
'893df240-babe-d796-cdf1-c436171b7a96'
>>> foo2 = Foo('I am different!')
>>> foo2.id
'9303f2a5-bfc9-e5ff-0ffa-a9846e2d2190'
>>> foo3 = Foo('I am unique!')
>>> foo1.id == foo3.id
True

is_loaded_from_dict

Resample.is_loaded_from_dict

Check if object was loaded from a dictionary.

Returns True if object was initialized from a dictionary, e.g. after loading it from a YAML file.

Returns

True if object was loaded from a dictionary,

otherwise False

resolvers

Resample.resolvers

Return resolvers.

Returns

Dictionary with resolvers.

short_id

Resample.short_id

Short object identifier.

The short ID consists of eight characters and is created from its non-hidden arguments.

Returns

short object identifier

Examples

>>> class Foo(Object):
...    def __init__(self, bar: str):
...        self.bar = bar
>>> foo1 = Foo('I am unique!')
>>> foo1.id
'893df240-babe-d796-cdf1-c436171b7a96'
>>> foo1.short_id
'171b7a96'
>>> foo2 = Foo('I am different!')
>>> foo2.short_id
'6e2d2190'
>>> foo3 = Foo('I am unique!')
>>> foo1.short_id == foo3.short_id
True

to_dict()

Resample.to_dict(*, include_version=True, flatten=False, root=None)

Converts object to a dictionary.

Includes items from audobject.Object.arguments. If an argument has a resolver, its value is encoded. Usually, the object can be re-instantiated using audobject.Object.from_dict(). However, if flatten=True, this is not possible.

Parameters

• include_version (bool) – add version to class name

• flatten (bool) – flatten the dictionary

• root (typing.Optional[str]) – if file is written to disk, set to target directory

Return type

typing.Dict[str, typing.Union[bool, datetime.datetime, dict, float, int, list, None, str]]

Returns

dictionary that represent the object

Examples

>>> import audobject.testing
>>> o = audobject.testing.TestObject('test', point=(1, 1))
>>> o.to_dict(include_version=False)
{'$audobject.core.testing.TestObject': {'name': 'test', 'point': [1, 1]}}
>>> o.to_dict(flatten=True)
{'name': 'test', 'point.0': 1, 'point.1': 1}

to_samples()

Resample.to_samples(value, sampling_rate=None, *, length=None, allow_negative=True)

Convert duration value to samples.

Return type

int

to_yaml()

Resample.to_yaml(path_or_stream, *, include_version=True)

Save object to YAML file.

Parameters

• path_or_stream (typing.Union[str, typing.IO]) – file path or stream

• include_version (bool) – add version to class name

to_yaml_s()

Resample.to_yaml_s(*, include_version=True)

Convert object to YAML string.

Parameters

include_version (bool) – add version to class name

Return type

str

Returns

YAML string

Examples

>>> import audobject.testing
>>> o = audobject.testing.TestObject('test', point=(1, 1))
>>> print(o.to_yaml_s(include_version=False))
$audobject.core.testing.TestObject:
  name: test
  point:
  - 1
  - 1