class nlpaug.augmenter.spectrogram.time_masking.TimeMaskingAug(name='TimeMasking_Aug', zone=(0.2, 0.8), coverage=1.0, verbose=0, silence=False, stateless=True)[source]

Bases: nlpaug.augmenter.spectrogram.spectrogram_augmenter.SpectrogramAugmenter

Augmenter that mask spectrogram based on frequency by random values.

  • zone (tuple) – Default value is (0.2, 0.8). Assign a zone for augmentation. By default, no any augmentation will be applied in first 20% and last 20% of whole audio.
  • coverage (float) – Default value is 1 and value should be between 0 and 1. Portion of augmentation. If 1 is assigned, augment operation will be applied to target audio segment. For example, the audio duration is 60 seconds while zone and coverage are (0.2, 0.8) and 0.7 respectively. 42 seconds ((0.8-0.2)*0.7*60) audio will be chosen for augmentation.
  • name (str) – Name of this augmenter
>>> import nlpaug.augmenter.spectogram as nas
>>> aug = nas.TimeMaskingAug()
augment(data, n=1, num_thread=1)
  • data (object/list) – Data for augmentation. It can be list of data (e.g. list of string or numpy) or single element (e.g. string or numpy). Numpy format only supports audio or spectrogram data. For text data, only support string or list of string.
  • n (int) – Default is 1. Number of unique augmented output. Will be force to 1 if input is list of data
  • num_thread (int) – Number of thread for data augmentation. Use this option when you are using CPU and n is larger than 1

Augmented data

>>> augmented_data = aug.augment(data)

From:, Time masking is applied so that t consecutive time steps [t0, t0 + t) are masked, where t is first chosen from a uniform distribution from 0 to the time mask parameter T, and t0 is chosen from [0, tau - t).