I have a manuscript which has been reviewed and one of the reviewer 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 convert it in a mel-spectrogram. I only modify the existing file and I do not increase my training dataset but I refer 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 if you could confirm or not my use of "data augmentation" and I would be grateful if you can provide a reference for this.

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.

I have a manuscript which has been reviewed and one of the reviewer 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 convert it in a mel-spectrogram. I only modify the existing file and I do not increase my training dataset but I refer 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 if you could confirm or not my use of "data augmentation" and I would be grateful if you can provide a reference for this.

Thank you already!

I have a manuscript which has been reviewed and one of the reviewer 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 convert it in a mel-spectrogram. I only modify the existing file and I do not increase my training dataset but I refer 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 if you could confirm or not my use of "data augmentation" and I would be grateful if you can provide a reference for this.