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A standalone deep dive into how transformer architectures have transformed audio processing, from Whisper to AudioLM and beyond.

Welcome to the Deep Learning for Audio series! This comprehensive guide will take you from audio fundamentals to building state-of-the-art models for speech, music, and sound processing.

Learn the fundamentals of audio: how sound works, digital audio representation, sampling theory, and audio formats. Build a strong foundation for audio deep learning.

Master essential signal processing techniques for audio: Fourier transforms, spectrograms, mel-scale, MFCCs, and other feature extraction methods crucial for deep learning.

Bridge audio processing and deep learning: understand neural network architectures for audio, build your first audio classifier, and learn best practices for audio ML.