Mixup audio augmentation and spectrogram augmentation are techniques used in machine learning when the available volume of data is limited. These techniques help to create more diverse training examples by blending audio samples or applying transformations to spectrograms. By augmenting the data, these methods increase the effective size of the training set, allowing models to learn from a wider range of variations and improve their generalization capabilities. This is particularly beneficial when working with small or limited datasets, as it helps prevent overfitting and enhances the model's ability to handle different audio inputs.
Link : https://colab.research.google.com/drive/19vgGYG_BCdbWlAmlZ19nRtyuVSuzZmle?usp=sharing
Mixup audio augmentation and spectrogram augmentation are techniques used in machine learning when the available volume of data is limited. These techniques help to create more diverse training examples by blending audio samples or applying transformations to spectrograms. By augmenting the data, these methods increase the effective size of the training set, allowing models to learn from a wider range of variations and improve their generalization capabilities. This is particularly beneficial when working with small or limited datasets, as it helps prevent overfitting and enhances the model's ability to handle different audio inputs. Link : https://colab.research.google.com/drive/19vgGYG_BCdbWlAmlZ19nRtyuVSuzZmle?usp=sharing