Deep Neural Audio

1. Audio Deep Learning Made Simple: Automatic Speech Recognition (ASR), How it Works

2. Learning from Audio Pitch and Chromagrams

3. Audio deep learning made simple by Ketan Doshi

   1. State-of-the-Art Techniques (What is sound and how it is digitized. What problems is audio deep learning solving in our daily lives. What are Spectrograms and why they are all-important.)

   2. Why Mel Spectrograms perform better (Processing audio data in Python. What are Mel Spectrograms and how to generate them)

   3. Data Preparation and Augmentation (Enhance Spectrograms features for optimal performance by hyper-parameter tuning and data augmentation)

   4. Sound Classification (End-to-end example and architecture to classify ordinary sounds. Foundational application for a range of scenarios.)

   5. Beam Search (Algorithm commonly used by Speech-to-Text and NLP applications to enhance predictions)

4. Wav2Vec - paper Youtube

5. Whisper

   1. ZAC (Zero-shot Audio Classification using Whisper) allows you to assign audio files to ANY class you want without training.

6. Neural Amp Modeler - is an open-source project that uses deep learning to create models of guitar amplifiers and pedals with state-of-the-art accuracy. Training notebook

7. NAM uses WaveNet

8. Echo State Network

   1. Youtube

   2. Gentle introduction to Echo State Networks

   3. Github code