I have a manuscript that has been reviewed and one of the reviewers commented on my use of the term " data augmentation", saying that it might not be the appropriate term in my case (explained below).
I collected a large dataset of short audio files which are used to train a Convolutional Neural Network. Before being used as model input, each audio file is processed through a pipeline that mixes it with other audio files, changes some of the sound properties (SNR ratio, distorting the audio ...) and finally converts it into a mel-spectrogram. I only modify the existing file and I do not increase my training dataset but I refer to this processing as "data augmentation".
I did not find any definitive definition of what is data augmentation. For instance, Salamon and Bello, 2016 define data augmentation as
the application of one or more deformations to a collection of annotated training samples which result in new, additional training data
However, it appears to me that the increase in the training dataset is only a byproduct of the data augmentation.
In any case, I would really appreciate it if you could confirm or not my use of "data augmentation" and I would be grateful if you can provide a reference for this.