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

Curated sources to learn audio technologies

My Favorite Resources for Learning

Modulation Series : Part 1 - Synth

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How our brain interprets sound?

Sampling and quantization

Acoustic environments and perception

Digital filter design

Time frequency analysis

Feature extraction in audio

Comprehensive list of sound properties

Parameters of sound