Audio Data Augmentation: Techniques, Theory, and Implementation

Learn about audio data augmentation techniques in this 26-minute conference talk from Data Science Conference Europe 2022. Explore solutions to the common challenge of insufficient data in machine learning and deep learning implementations, particularly focusing on audio data where collection can be costly and time-consuming. Discover various audio augmentation methods including time shifting, time stretching, pitch scaling, noise addition, polarity inversion, and random gain for raw audio, as well as time and frequency masking for spectrograms. Master the theoretical foundations, practical implementation strategies, and best practices for audio data augmentation, while understanding their benefits and applications. Gain hands-on knowledge of Python libraries for audio augmentation and learn how to effectively transform and enhance audio datasets for improved machine learning model performance.