This post discusses the use of data augmentation techniques for audio in machine learning. The author explores various operations such as noise addition, time-shifting, and changing speed to increase the diversity of training data. Code examples using libraries like NumPy, librosa, and AugLy are provided. The key takeaways include the understanding that data augmentation cannot replace real training data and the importance of selecting appropriate augmentation methods based on the data patterns.

Audio Data Augmentation

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