preprocessing:

• DataEncoder: Responsible for creating and saving the pre-encoded versions of the audio files used for training.
• EncoderDataset: Responsible for loading the raw audio files, noise files, applying preprocessing steps (such as cutting to equal length, applying bandpass filter) and creating the noisy audio files.

train:

• EncodedDataset: Responsible for loading the pre-encoded input and target files for training the model.
• LatentNetwork: Contains the structure of the latent network used in the audio denoising problem.
• LatentTrainer: Responsible for training the latent network with the pre-encoded dataset.
• utils:
  • ModelSaveHandler: Utility class for saving model checkpoints.

1. Noise: Hitting table

2. Noise: Pressing plastic bag

3. Noise: Hitting glass with a fork

4. Noise: Playing pop music

5. Noise: Playing classical music

6. Noise: Playing dubstep music