Smile

class opensmile.Smile(feature_set=FeatureSet.ComParE_2016, feature_level=FeatureLevel.Functionals, *, options=None, loglevel=2, logfile=None, sampling_rate=None, channels=0, mixdown=False, resample=False, segment=None, keep_nat=False, num_workers=1, multiprocessing=False, verbose=False)[source]

OpenSMILE feature extractor.

  1. You can choose a pre-defined feature set by passing one of opensmile.FeatureSet

  2. You can also provide a custom config file using the following template:

    [componentInstances:cComponentManager]
instance[dataMemory].type=cDataMemory

;;; default source
\{\cm[source{?}:source include config]}

;;; add components reading from reader.dmLevel=wave

;;; combine features
[componentInstances:cComponentManager]
instance[funcconcat].type=cVectorConcat
[funcconcat:cVectorConcat]
reader.dmLevel = <feat-1>;<feat-2>;...
writer.dmLevel = features
includeSingleElementFields = 1

;;; default sink
\{\cm[sink{?}:include external sink]}

Note

The following arguments are not serialized:

  • keep_nat

  • loglevel

  • logfile

  • num_workers

  • multiprocessing

  • segment

  • verbose

For more information see section on hidden arguments.

Parameters

  • feature_set (Union[str, FeatureSet]) – default feature set or path to a custom config file

  • feature_level (Union[str, FeatureLevel]) – default feature level or level name if a custom config file is used

  • options (Optional[dict]) – dictionary with optional script parameters

  • loglevel (int) – log level (0-5), the higher the number the more log messages are given

  • logfile (Optional[str]) – if not None log messages will be stored to this file

  • sampling_rate (Optional[int]) – sampling rate in Hz. If None it will call process_func with the actual sampling rate of the signal.

  • channels (Union[int, Sequence[int]]) – channel selection, see audresample.remix()

  • mixdown (bool) – apply mono mix-down on selection

  • resample (bool) – if True enforces given sampling rate by resampling

  • segment (Optional[Segment]) – when a audinterface.Segment object is provided, it will be used to find a segmentation of the input signal. Afterwards processing is applied to each segment

  • keep_nat (bool) – if the end of segment is set to NaT do not replace with file duration in the result

  • num_workers (Optional[int]) – number of parallel jobs or 1 for sequential processing. If None will be set to the number of processors on the machine multiplied by 5 in case of multithreading and number of processors in case of multiprocessing

  • multiprocessing (bool) – use multiprocessing instead of multithreading

  • verbose (bool) – show debug messages

Examples: >>> sampling_rate = 16000 >>> signal = np.zeros(sampling_rate) >>> smile = Smile( … feature_set=FeatureSet.ComParE_2016, … feature_level=FeatureLevel.Functionals, … ) >>> smile.process_signal(signal, sampling_rate).audspec_lengthL1norm_sma_range start end 0 days 0 days 00:00:01 0.0 Name: audspec_lengthL1norm_sma_range, dtype: float32

__call__()

Smile.__call__(signal, sampling_rate)[source]

Apply processing to signal.

This function processes the signal without transforming the output into a pd.DataFrame. Instead it will return the raw processed signal. However, if channel selection, mixdown and/or resampling is enabled, the signal will be first remixed and resampled if the input sampling rate does not fit the expected sampling rate.

Parameters

  • signal (ndarray) – signal values

  • sampling_rate (int) – sampling rate in Hz

Return type

ndarray

Returns

Processed signal

Raises

arguments

Smile.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

Smile.borrowed_arguments

Returns borrowed arguments.

Returns

Dictionary with borrowed arguments.

column_names

Smile.column_names

Feature column names.

config_name

Smile.config_name

Return name of config file (without file extension).

config_path

Smile.config_path

Return file path of config file.

default_config_root

Smile.default_config_root

Return root directory with standard config files.

feature_level

Smile.feature_level

Standard feature level or sink level in custom config file.

feature_names

Smile.feature_names

Feature names.

feature_set

Smile.feature_set

Standard feature set or path to custom config file

from_dict()

static Smile.from_dict(d, root=None, **kwargs)
Return type

Object

from_yaml()

static Smile.from_yaml(path_or_stream, **kwargs)
Return type

Object

from_yaml_s()

static Smile.from_yaml_s(yaml_string, **kwargs)
Return type

Object

hidden_arguments

Smile.hidden_arguments

Returns hidden arguments.

Returns

List with names of hidden arguments.

hop_dur

Smile.hop_dur

Hop duration.

id

Smile.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!')
>>> print(foo1.id)
893df240-babe-d796-cdf1-c436171b7a96
>>> foo2 = Foo('I am different!')
>>> print(foo2.id)
9303f2a5-bfc9-e5ff-0ffa-a9846e2d2190
>>> foo3 = Foo('I am unique!')
>>> print(foo1.id == foo3.id)
True

is_loaded_from_dict

Smile.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

logfile

Smile.logfile

Log file

loglevel

Smile.loglevel

Log level

name

Smile.name

Name of the feature set.

num_channels

Smile.num_channels

Expected number of channels

num_features

Smile.num_features

Number of features.

options

Smile.options

Dictionary with options

params

Smile.params

Dictionary of parameters describing the feature set.

process

Smile.process

Processing object.

process_file()

Smile.process_file(file, *, start=None, end=None, root=None)

Extract features from an audio file.

Parameters

  • file (str) – file path

  • start (Union[float, int, str, Timedelta, None]) – start processing at this position. If value is a float or integer it is treated as seconds. See audinterface.utils.to_timedelta() for further options

  • end (Union[float, int, str, Timedelta, None]) – end processing at this position. If value is a float or integer it is treated as seconds. See audinterface.utils.to_timedelta() for further options

  • root (Optional[str]) – root folder to expand relative file path

Raises
Return type

DataFrame

process_files()

Smile.process_files(files, *, starts=None, ends=None, root=None)

Extract features for a list of files.

Parameters
Raises
Return type

DataFrame

process_folder()

Smile.process_folder(root, *, filetype='wav', include_root=True)

Extract features from files in a folder.

Note

At the moment does not scan in sub-folders!

Parameters

  • root (str) – root folder

  • filetype (str) – file extension

  • include_root (bool) – if True the file paths are absolute in the index of the returned result

Raises
Return type

DataFrame

process_func_applies_sliding_window

Smile.process_func_applies_sliding_window

Controls if processing function applies sliding window.

process_index()

Smile.process_index(index, *, preserve_index=False, root=None, cache_root=None)

Extract features from an index conform to audformat.

If cache_root is not None, a hash value is created from the index using audformat.utils.hash() and the result is stored as <cache_root>/<hash>.pkl. When called again with the same index, features will be read from the cached file.

Parameters

  • index (Index) – index with segment information

  • preserve_index (bool) – if True and audinterface.Feature.process.segment is None the returned index will be of same type as the original one, otherwise always a segmented index is returned

  • root (Optional[str]) – root folder to expand relative file paths

  • cache_root (Optional[str]) – cache folder (see description)

Raises
Return type

DataFrame

process_signal()

Smile.process_signal(signal, sampling_rate, *, file=None, start=None, end=None)

Extract features for an audio signal.

Note

If a file is given, the index of the returned frame has levels file, start and end. Otherwise, it consists only of start and end.

Parameters

  • signal (ndarray) – signal values

  • sampling_rate (int) – sampling rate in Hz

  • file (Optional[str]) – file path

  • start (Union[float, int, str, Timedelta, None]) – start processing at this position. If value is a float or integer it is treated as seconds. See audinterface.utils.to_timedelta() for further options

  • end (Union[float, int, str, Timedelta, None]) – end processing at this position. If value is a float or integer it is treated as seconds. See audinterface.utils.to_timedelta() for further options

Raises

  • RuntimeError – if sampling rates do not match

  • RuntimeError – if channel selection is invalid

  • RuntimeError – if dimension of extracted features is greater than three

  • RuntimeError – if feature extractor uses sliding window, but self.win_dur is not specified

  • RuntimeError – if number of features does not match number of feature names

  • RuntimeError – if multiple frames are returned, but win_dur is not set

Return type

DataFrame

process_signal_from_index()

Smile.process_signal_from_index(signal, sampling_rate, index)

Split a signal into segments and extract features for each segment.

Parameters

  • signal (ndarray) – signal values

  • sampling_rate (int) – sampling rate in Hz

  • index (MultiIndex) – a pandas.MultiIndex with two levels named start and end that hold start and end positions as pandas.Timedelta objects. See also audinterface.utils.signal_index()

Raises
Return type

DataFrame

resolvers

Smile.resolvers

Return resolvers.

Returns

Dictionary with resolvers.

to_dict()

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

Return type

Dict[str, Union[bool, 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_numpy()

Smile.to_numpy(frame)

Return feature values as a numpy array.

The returned numpy.ndarray has the original shape, i.e. (channels, features, time).

Parameters

frame (DataFrame) – feature frame

Return type

ndarray

to_yaml()

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

Save object to YAML file.

Parameters

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

  • include_version (bool) – add version to class name

to_yaml_s()

Smile.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

verbose

Smile.verbose

Show debug messages.

win_dur

Smile.win_dur

Window duration.