FeaturesBase Class¶

MFCC Audio Feature Extractor

class spiegel.features.mfcc.MFCC(numMFCCs=20, sampleRate=44100, frameSizeSamples=2048, hopSizeSamples=512)¶

Parameters

numMFCCs (int, optional) – number of mffcs to return per frame, defaults to 20
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

getFeatures(audio, normalize=False)¶

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