Musical Data Augmentation¶

The muda package implements annotation-aware musical data augmentation, as described in the muda paper [1]. The goal of this package is to make it easy for practitioners to consistently apply perturbations to annotated music data for the purpose of fitting statistical models.

[1]	McFee, B., Humphrey, E.J., and Bello, J.P. “A software framework for Musical Data Augmentation.” 16th International Society for Music Information Retrival conference (ISMIR). 2015.

Introduction¶

Note

Before reading ahead, it is recommended to familiarize yourself with the JAMS documentation.

The design of muda is patterned loosely after the Transformer abstraction in scikit-learn. At a high level, each input example consists of an audio clip (with sampling rate) as a numpy.ndarray and its annotations stored in JAMS format. To streamline the deformation process, audio data is first stored within the JAMS object so that only a single payload needs to be transferred throughout the system.

Deformation objects (muda.core.BaseTransformer) have a single user-facing method, transform(), which accepts an input JAMS object and generates a sequence of deformations of that object. By operating on JAMS objects, the deformation object can simultaneously modify both the audio and all of its corresponding annotations.

After applying deformations, the modified audio and annotations can be stored to disk by calling muda.save(). Alternatively, because transformations are generators, results can be processed online by a stochastic learning algorithm.

Requirements¶

Installing muda via pip install muda should automatically satisfy the python dependencies:

JAMS 0.2

librosa 0.4

pyrubberband 0.1

pysoundfile 0.8

jsonpickle

However, certain deformers require external applications that must be installed separately.

sox

rubberband-cli

Examples¶

Example usage¶

This section gives a quick introduction to using muda through example applications.

Loading data¶

In muda, all data pertaining to a track is contained within a jams object. Before processing any tracks with muda, the jams object must be prepared using one of muda.load_jam_audio or muda.jam_pack. These functions prepare the jams object to contain (deformed) audio and store the deformation history objects.

>>> # Loading data from disk
>>> j_orig = muda.load_jam_audio('orig.jams', 'orig.ogg')
>>> # Ready to go!

>>> # Loading audio from disk with an existing jams
>>> j_orig = jams.load('existing_jams_file.jams')
>>> j_orig = muda.load_jam_audio(existing_jams, 'orig.ogg')
>>> # Ready to go!

>>> # Loading in-memory audio (y, sr) with an existing jams
>>> j_orig = jams.load('existing_jams_file.jams')
>>> j_orig = muda.jam_pack(existing_jams, _audio=dict(y=y, sr=sr))
>>> # Ready to go!

Applying a deformation¶

Once the data has been prepared, we are ready to start applying deformations. This example uses a simple linear pitch shift deformer to generate five perturbations of an input. Each deformed example is then saved to disk.

>>> pitch = muda.deformers.LinearPitchShift(n_samples=5, lower=-1, upper=1)
>>> for i, jam_out in enumerate(pitch.transform(j_orig)):
...     muda.save('output_{:02d}.ogg'.format(i),
...               'output_{:02d}.jams'.format(i),
...               jam_out)

The deformed audio data can be accessed directly in the dictionary jam_out.sandbox.muda._audio. Note that a full history of applied transformations is recorded within jam_out.sandbox.muda under the state and history objects.

Pipelines¶

The following example constructs a two-stage deformation pipeline. The first stage applies random pitch shifts, while the second stage applies random time stretches. The pipeline therefore generates 25 examples from the input j_orig.

>>> # Load an example audio file with annotation
>>> j_orig = muda.load_jam_audio('orig.jams', 'orig.ogg')

>>> # Construct a deformation pipeline
>>> pitch_shift = muda.deformers.RandomPitchShift(n_samples=5)
>>> time_stretch = muda.deformers.RandomTimeStretch(n_samples=5)
>>> pipeline = muda.Pipeline(steps=[('pitch_shift', pitch_shift),
...                                 ('time_stretch', time_stretch)])

>>> for j_new in pipeline.transform(j_orig):
...     process(j_new)

Unions¶

Union operators are similar to Pipelines, in that they allow multiple deformers to be combined as a single object that generates a sequence of deformations. The difference between Union and Pipeline is that a pipeline composes deformations together, so that a single output is the result of multiple stages of processing; a union only applies one deformation at a time to produce a single output.

The following example is similar to the pipeline example above:

>>> # Load an example audio file with annotation
>>> j_orig = muda.load_jam_audio('orig.jams', 'orig.ogg')
>>> # Construct a deformation pipeline
>>> pitch_shift = muda.deformers.RandomPitchShift(n_samples=5)
>>> time_stretch = muda.deformers.RandomTimeStretch(n_samples=5)
>>> union = muda.Union(steps=[('pitch_shift', pitch_shift),
...                           ('time_stretch', time_stretch)])
>>> for j_new in union.transform(j_orig):
...     process(j_new)

Each of the resulting j_new objects produced by the union has had either its pitch shifted by the pitch_shift object or its time stretched by the time_stretch object, but not both.

Unions apply deformations in a round-robin schedule, so that the first output is produced by the first deformer, the second output is produced by the second deformer, and so on, until the list of deformers is exhausted and the first deformer produces its second output.

Bypass operators¶

When using pipelines, it is sometimes beneficial to allow a stage to be skipped, so that the input to one stage can be fed through to the next stage without intermediate processing. This is easily accomplished with Bypass objects, which first emit the input unchanged, and then apply the contained deformation as usual. This is demonstrated in the following example, which is similar to the pipeline example, except that it guarantees that each stage is applied to j_orig in isolation, as well as in composition. It therefore generates 36 examples (including j_orig itself as the first output).

>>> # Load an example audio file with annotation
>>> j_orig = muda.load_jam_audio('orig.jams', 'orig.ogg')
>>> # Construct a deformation pipeline
>>> pitch_shift = muda.deformers.RandomPitchShift(n_samples=5)
>>> time_stretch = muda.deformers.RandomTimeStretch(n_samples=5)
>>> pipeline = muda.Pipeline(steps=[('pitch_shift', muda.deformers.Bypass(pitch_shift)),
...                                 ('time_stretch', muda.deformers.Bypass(time_stretch))])
>>> for j_new in pipeline.transform(j_orig):
...     process(j_new)

Saving deformations¶

All deformation objects, including bypasses and pipelines, can be serialized to plain-text (JSON) format, saved to disk, and reconstructed later. This is demonstrated in the following example.

>>> # Encode an existing pitch shift deformation object
>>> pitch_shift = muda.deformers.RandomPitchShift(n_samples=5)
>>> ps_str = muda.serialize(pitch_shift)
>>> print(ps_str)
{"params": {"n_samples": 5, "mean": 0.0, "sigma": 1.0},
 "__class__": {"py/type": "muda.deformers.pitch.RandomPitchShift"}}

>>> # Reconstruct the pitch shifter from its string encoding
>>> ps2 = muda.deserialize(ps_str)

>>> # Encode a full pipeline as a string
>>> pipe_str = muda.serialize(pipeline)

>>> # Decode the string to reconstruct a new pipeline object
>>> new_pipe = muda.deserialize(pipe_str)

>>> # Process jams with the new pipeline
>>> for j_new in new_pipe.transform(j_orig):
...     process(j_new)

API Reference¶

MUDA Core¶

Functions¶

Core functionality for muda

muda.core.load_jam_audio(jam_in, audio_file, validate=True, strict=True, fmt='auto', **kwargs)[source]¶

Load a jam and pack it with audio.

Parameters:	jam_in : str, file descriptor, or jams.JAMS JAMS filename, open file-descriptor, or object to load. See `jams.load` for acceptable formats. audio_file : str Audio filename to load validate : bool strict : bool fmt : str Parameters to jams.load kwargs : additional keyword arguments See librosa.load
Returns:	jam : jams.JAMS A jams object with audio data in the top-level sandbox

See also

jams.load
librosa.core.load

Notes

This operation can modify the file_metadata.duration field of jam_in: If it is not currently set, it will be populated with the duration of the audio file.

muda.core.save(filename_audio, filename_jam, jam, strict=True, fmt='auto', **kwargs)[source]¶

Save a muda jam to disk

Parameters:	filename_audio: str The path to store the audio file filename_jam: str The path to store the jams object strict: bool Strict safety checking for jams output fmt : str Output format parameter for jams.JAMS.save kwargs Additional parameters to soundfile.write

muda.core.jam_pack(jam, **kwargs)[source]¶

Pack data into a jams sandbox.

If not already present, this creates a muda field within jam.sandbox, along with history, state, and version arrays which are populated by deformation objects.

Any additional fields can be added to the muda sandbox by supplying keyword arguments.

Parameters:	jam : jams.JAMS A JAMS object
Returns:	jam : jams.JAMS The updated JAMS object

Examples

>>> jam = jams.JAMS()
>>> muda.jam_pack(jam, my_data=dict(foo=5, bar=None))
>>> jam.sandbox
<Sandbox: muda>
>>> jam.sandbox.muda
<Sandbox: state, version, my_data, history>
>>> jam.sandbox.muda.my_data
{'foo': 5, 'bar': None}

muda.core.serialize(transform, **kwargs)[source]¶

Serialize a transformation object or pipeline.

Parameters:	transform : BaseTransform or Pipeline The transformation object to be serialized kwargs Additional keyword arguments to jsonpickle.encode()
Returns:	json_str : str A JSON encoding of the transformation

See also

deserialize

Examples

>>> D = muda.deformers.TimeStretch(rate=1.5)
>>> muda.serialize(D)
'{"params": {"rate": 1.5},
  "__class__": {"py/type": "muda.deformers.time.TimeStretch"}}'

muda.core.deserialize(encoded, **kwargs)[source]¶

Construct a muda transformation from a JSON encoded string.

Parameters:	encoded : str JSON encoding of the transformation or pipeline kwargs Additional keyword arguments to jsonpickle.decode()
Returns:	obj The transformation

See also

serialize

Examples

>>> D = muda.deformers.TimeStretch(rate=1.5)
>>> D_serial = muda.serialize(D)
>>> D2 = muda.deserialize(D_serial)
>>> D2
TimeStretch(rate=1.5)

Classes¶

class muda.base.BaseTransformer[source]¶

The base class for all transformation objects. This class implements a single transformation (history) and some various niceties.

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

states

get_params(self, deep=True)[source]¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)[source]¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

class muda.base.Pipeline(steps=None)[source]¶

Wrapper which allows multiple BaseDeformer objects to be chained together

A given JAMS object will be transformed sequentially by each stage of the pipeline.

The pipeline induces a graph over transformers

See also

Union

Examples

>>> P = muda.deformers.PitchShift(semitones=5)
>>> T = muda.deformers.TimeStretch(speed=1.25)
>>> Pipe = muda.Pipeline(steps=[('Pitch:maj3', P), ('Speed:1.25x', T)])
>>> output_jams = list(Pipe.transform(jam_in))

Attributes:	steps : argument array steps[i] is a tuple of (name, Transformer)

Methods

`get_params`(self)	Get the parameters for this object.
`transform`(self, jam)	Apply the sequence of transformations to a single jam object.

get_params(self)[source]¶: Get the parameters for this object. Returns as a dict.

transform(self, jam)[source]¶

Apply the sequence of transformations to a single jam object.

Parameters:	jam : jams.JAMS The jam object to transform
Yields:	jam_out : jams.JAMS The jam objects produced by the transformation sequence

class muda.base.Union(steps=None)[source]¶

Wrapper which allows multiple BaseDeformer objects to be combined for round-robin sampling.

A given JAMS object will be transformed sequentially by each element of the union, in round-robin fashion. This is similar to Pipeline, except the deformers are independent of one another in a Union, rather than applied sequentially.

See also

Pipeline

Examples

>>> P = muda.deformers.PitchShift(semitones=5)
>>> T = muda.deformers.TimeStretch(speed=1.25)
>>> union = muda.Union(steps=[('Pitch:maj3', P), ('Speed:1.25x', T)])
>>> output_jams = list(union.transform(jam_in))

Attributes:	steps : argument array steps[i] is a tuple of (name, Transformer)

Methods

`get_params`(self)	Get the parameters for this object.
`transform`(self, jam)	Apply the sequence of transformations to a single jam object.

get_params(self)[source]¶: Get the parameters for this object. Returns as a dict.

transform(self, jam)[source]¶

Apply the sequence of transformations to a single jam object.

Parameters:	jam : jams.JAMS The jam object to transform
Yields:	jam_out : jams.JAMS The jam objects produced by each member of the union

Deformation reference¶

Utilities¶

class muda.deformers.Bypass(transformer=None)[source]¶

Bypass transformer. Wraps an existing transformer object.

This allows pipeline stages to become optional.

The first example generated by a Bypass’s transform method is the input, followed by all examples generated by the contained transformer object.

Examples

>>> # Generate examples with and without a pitch-shift
>>> D = muda.deformers.Pitchshift(n_semitones=2.0)
>>> B = muda.deformers.Bypass(transformer=D)
>>> out_jams = list(B.transform(input_jam))

Attributes:	transformer : muda.BaseTransformer The transformer object to bypass

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Bypass transformations.

states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)[source]¶

Bypass transformations.

Parameters:	jam : pyjams.JAMS A muda-enabled JAMS object
Yields:	jam_out : pyjams.JAMS iterator The first result is jam (unmodified), by reference All subsequent results are generated by transformer

Audio deformers¶

class muda.deformers.BackgroundNoise(n_samples=1, files=None, weight_min=0.1, weight_max=0.5)[source]¶

Additive background noise deformations.

From each background noise signal, n_samples clips are randomly extracted and mixed with the input audio with a random mixing coefficient sampled uniformly between weight_min and weight_max.

This transformation affects the following attributes:

Audio

Attributes:	n_samples : int > 0 The number of samples to generate with each noise source files : str or list of str Path to audio file(s) on disk containing background signals weight_min : float in (0.0, 1.0) weight_max : float in (0.0, 1.0) The minimum and maximum weight to combine input signals y_out = (1 - weight) * y + weight * y_noise

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

class muda.deformers.DynamicRangeCompression(preset=None)[source]¶

Dynamic range compression.

For each DRC preset configuration, one deformation is generated.

This transformation affects the following attributes:

Audio

Examples

>>> # A single preset
>>> drc = muda.deformers.DynamicRangeCompression(preset='radio')
>>> # Multiple presets
>>> drc = muda.deformers.DynamicRangeCompression(preset=['film standard',
...                                                      'film light'])
>>> # All presets
>>> drc = muda.deformers.DynamicRangeCompression(preset=muda.deformers.PRESETS.keys())

Attributes:	preset : str or list of str One or more supported preset values: - radio - film standard - film light - music standard - music light - speech

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

Time-stretch deformers¶

class muda.deformers.TimeStretch(rate=1.2)[source]¶

Static time stretching by a fixed rate

This transformation affects the following attributes:

Annotations
- all: time, duration
- tempo: values
metadata
- duration
Audio

See also

LogspaceTimeStretch
RandomTimeStretch

Examples

>>> D = muda.deformers.TimeStretch(rate=2.0)
>>> out_jams = list(D.transform(jam_in))

Attributes:	rate : float or list of floats, strictly positive The rate at which to speedup the audio. - rate > 1 speeds up, - rate < 1 slows down.

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
deform_tempo
deform_times
metadata
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

class muda.deformers.RandomTimeStretch(n_samples=3, location=0.0, scale=0.1)[source]¶

Random time stretching

For each deformation, the rate parameter is drawn from a log-normal distribution with parameters (location, scale)

Annotations
- all: time, duration
- tempo: values
metadata
- duration
Audio

See also

TimeStretch
LogspaceTimeStretch
numpy.random.lognormal

Attributes:	n_samples : int > 0 The number of samples to generate location : float scale : float > 0 Parameters of a log-normal distribution from which rate parameters are sampled.

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
deform_tempo
deform_times
metadata
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

class muda.deformers.LogspaceTimeStretch(n_samples=3, lower=-0.3, upper=0.3)[source]¶

Logarithmically spaced time stretching.

n_samples are generated with stretching spaced logarithmically between 2.0**lower and 2`.0**upper`.

This transformation affects the following attributes:

Annotations
- all: time, duration
- tempo: values
metadata
- duration
Audio

See also

TimeStretch
RandomTimeStretch

Attributes:	n_samples : int > 0 Number of deformations to generate lower : float upper : float > lower Minimum and maximum bounds on the stretch parameters

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
deform_tempo
deform_times
metadata
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

Pitch-shift deformers¶

class muda.deformers.PitchShift(n_semitones=1)[source]¶

Static pitch shifting by (fractional) semitones

This transformation affects the following attributes:

Annotations
- key_mode
- chord, chord_harte, chord_roman
- pitch_hz, pitch_midi, pitch_class
Audio

See also

RandomPitchShift
LinearPitchShift

Examples

>>> # Shift down by a quarter-tone
>>> D = muda.deformers.PitchShift(n_semitones=-0.5)

Attributes:	n_semitones : float or list of float The number of semitones to transpose the signal. Can be positive, negative, integral, or fractional.

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
deform_frequency
deform_midi
deform_note
deform_tonic
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

class muda.deformers.RandomPitchShift(n_samples=3, mean=0.0, sigma=1.0)[source]¶

Randomized pitch shifter

Pitch is transposed by a normally distributed random variable.

This transformation affects the following attributes:

Annotations
- key_mode
- chord, chord_harte, chord_roman
- pitch_hz, pitch_midi, pitch_class
Audio

See also

PitchShift
LinearPitchShift

Examples

>>> # 5 random shifts with unit variance and mean of 1 semitone
>>> D = muda.deformers.PitchShift(n_samples=5, mean=1.0, sigma=1)

Attributes:	n_samples : int > 0 The number of samples to generate per input mean : float sigma : float > 0 The parameters of the normal distribution for sampling pitch shifts

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
deform_frequency
deform_midi
deform_note
deform_tonic
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

class muda.deformers.LinearPitchShift(n_samples=3, lower=-1, upper=1)[source]¶

Linearly spaced pitch shift generator

This transformation affects the following attributes:

Annotations
- key_mode
- chord, chord_harte, chord_roman
- pitch_hz, pitch_midi, pitch_class
Audio

See also

PitchShift
RandomPitchShift

Examples

>>> # 5 shifts spaced between -2 and +2 semitones
>>> D = muda.deformers.LinearPitchShift(n_samples=5, lower=-2, upper=2)

Attributes:	n_samples : int > 0 The number of samples to generate per input lower : float upper : float The lower and upper bounds for the shift sampling

Methods

`get_params`(self[, deep])	Get the parameters for this object.
`transform`(self, jam)	Iterative transformation generator

audio
deform_frequency
deform_midi
deform_note
deform_tonic
states

get_params(self, deep=True)¶

Get the parameters for this object. Returns as a dict.

Parameters:	deep : bool Recurse on nested objects
Returns:	params : dict A dictionary containing all parameters for this object

transform(self, jam)¶

Iterative transformation generator

Applies the deformation to an input jams object.

This generates a sequence of deformed output JAMS.

Parameters:	jam : jams.JAMS The jam to transform

Examples

>>> for jam_out in deformer.transform(jam_in):
...     process(jam_out)

Release notes¶

v0.2.0¶

#61 Exposed JAMS input-output arguments in load_jam_audio and save.
#59 Fixed an off-by-one error in background noise sampling – Vincent Lostanlen

v0.1.4¶

#58 Updated for compatibility with JAMS 0.3.0

v0.1.3¶

#40 BackgroundNoise now stores sample positions in its output history
#44 fixed a bug in reconstructing muda-output jams files
#47 removed dependency on scikit-learn
#48, #54 converted unit tests from nose to py.test
#49 TimeStretch and PitchShift deformers now support multiple values
#52 added the Union class

v0.1.2¶

This is a minor bug-fix revision.

The defaults for LogspaceTimeStretch have been changed to a more reasonable setting.
Track duration is now overridden when loading audio into a jams object.

v0.1.1¶

This is a minor bug-fix revision.

pypi distribution now includes the drc_presets data.

v0.1.0¶

Initial public release.

Musical Data Augmentation¶

Introduction¶

Requirements¶

Examples¶

Example usage¶

Loading data¶

Applying a deformation¶

Pipelines¶

Unions¶

Bypass operators¶

Saving deformations¶

API Reference¶

MUDA Core¶

Functions¶

Classes¶

Deformation reference¶

Utilities¶

Audio deformers¶

Time-stretch deformers¶

Pitch-shift deformers¶

Release notes¶

Release notes¶

v0.2.0¶

v0.1.4¶

v0.1.3¶

v0.1.2¶

v0.1.1¶

v0.1.0¶

Contribute¶