FeaturesBase Class¶

Abstract Base Class for Audio Features

class spiegel.features.features_base.FeaturesBase(dimensions, sampleRate=44100, frameSizeSamples=2048, hopSizeSamples=512)¶

Bases: abc.ABC

Parameters

dimensions (int) – Number of dimensions associated with these features
sampleRate (int, optional) – Audio sample rate, defaults to 44100
frameSizeSamples (int, optional) – frame size in audio samples, defaults to 2048
hopSizeSamples (int, optional) – hop size in audio samples, defaults to 512

fitNormalizers(data, transform=False)¶

Fit normalizers to dataset for future transforms. Can also transform the data and return a normalized version of that data.

Parameters

data (np.array) – data to train normalizer on
transform (bool, optional) – should the incoming data also be normalized? Defuaults to False

Returns

None if no transform applied, np.array with normalized data if transform applied

Return type

None or np.array

abstract getFeatures(audio, normalize=False)¶

Must be implemented. Run audio feature extraction on audio provided as parameter. Normalization should be applied based on the normalize parameter.

Parameters

audio (np.array) – Audio to process features on
normalize (bool, optional) – Whether or not the features are normalized, defaults to False

Returns

results from audio feature extraction

Return type

np.array

loadNormalizers(location)¶

Load trained normalizers from disk

normalize(data)¶

Normalize features using pre-trained normalizer

saveNormalizers(location)¶

Save the trained normalizers for these features for later use

setNormalizer(dimension, normalizer)¶

Set a normalizer for a dimension, this will be used to normalize that dimension

Parameters