Audio Data Augmentation Techniques: The Theory @ValerioVelardoTheSoundofAI
Audio Data Augmentation Techniques: The Theory  @ValerioVelardoTheSoundofAI
Uploaded January 2022 | Updated September 2026, 2 weeks ago
Learn audio augmentation techniques both in the raw audio and spectrogram domains.

Specifically, you’ll learn about:
📌 Time shifting
📌 Time stretching
📌 Pitch scaling
📌 Noise addition
📌 Impulse response addition
📌 Filters
📌 Polarity Inversion
📌 Random gain
📌Time masking
📌 Frequency masking

Slides:
github.com/musikalkemist/audioDataAugmentationTutorial/blob/main/2/Audio%20Data%20Augmentation%20Techniques.pdf

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0:00 Intro
0:19 Raw audio vs spectrogram augmentation
1:06 Time shifting
1:36 Time stretching
2:12 Pitch scaling
3:04 Noise addition
3:44 Impulse response addition
4:42 Filters
5:21 Polarity Inversion
5:53 Random gain
7:21 SpecAugment
8:27 Time masking
9:37 Frequency masking
10:55 Other spectrogram augmentation techniques
12:07 Join The Sound of AI community!
12:40 Coming up
Audio Data Augmentation Techniques: The TheorySound and Waveforms5 Reasons Why You Should Participate in a HackathonDeploying the Speech Recognition System on Amazon AWSMaking Predictions with the Speech Recognition SystemPreprocessing Song Dataset for Melody Generation, pt. 118.  Transformers Explained Easily: Part 2 - Generative Music AIR&D and production in AI (audio) companies: How to balance7 Tricks to Make a Data Science Project Maintainable7 Great AI Music Projects for your Portfolio16- How to Implement a CNN for Music Genre ClassificationUnderstanding Audio Signals for Machine Learning
Valerio Velardo - The Sound of AI |

Audio Data Augmentation Techniques: The Theory

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