# Deep Neural Audio

1. [Audio Deep Learning Made Simple: Automatic Speech Recognition (ASR), How it Works](https://towardsdatascience.com/audio-deep-learning-made-simple-automatic-speech-recognition-asr-how-it-works-716cfce4c706)
2. [Learning from Audio Pitch and Chromagrams](https://towardsdatascience.com/learning-from-audio-pitch-and-chromagrams-5158028a505)
3. Audio deep learning made simple by Ketan Doshi
   1. [State-of-the-Art Techniques](https://towardsdatascience.com/audio-deep-learning-made-simple-part-1-state-of-the-art-techniques-da1d3dff2504) *(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](https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectrograms-perform-better-aad889a93505) *(Processing audio data in Python. What are Mel Spectrograms and how to generate them)*
   3. [Data Preparation and Augmentation](https://towardsdatascience.com/audio-deep-learning-made-simple-part-3-data-preparation-and-augmentation-24c6e1f6b52) *(Enhance Spectrograms features for optimal performance by hyper-parameter tuning and data augmentation)*
   4. [Sound Classification](https://towardsdatascience.com/audio-deep-learning-made-simple-sound-classification-step-by-step-cebc936bbe5) *(End-to-end example and architecture to classify ordinary sounds. Foundational application for a range of scenarios.)*
   5. [Beam Search](https://towardsdatascience.com/foundations-of-nlp-explained-visually-beam-search-how-it-works-1586b9849a24) *(Algorithm commonly used by Speech-to-Text and NLP applications to enhance predictions)*
4. *Wav2Vec - paper* [*Youtube*](https://www.youtube.com/watch?v=fMqYul2TvBE)
5. [Whisper](https://github.com/openai/whisper) 
   1. [ZAC](https://github.com/jumon/zac) (Zero-shot Audio Classification using Whisper) allows you to assign audio files to ANY class you want without training.
6. [Neural Amp Modeler](https://www.neuralampmodeler.com/) - 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](https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/easy_colab.ipynb#scrollTo=zrXbQY7vjZjk) 
7. [NAM uses WaveNet](https://arxiv.org/abs/1609.03499)
8. Echo State Network
   1. [Youtube](https://www.youtube.com/watch?v=uF4i9_7IQlI)
   2. [Gentle introduction to Echo State Networks](https://towardsdatascience.com/gentle-introduction-to-echo-state-networks-af99e5373c68)
   3. [Github code](https://github.com/ciortanmadalina/EchoStateNetwork/blob/master/EchoStateNetwork.ipynb)

      [<br>](https://www.neuralampmodeler.com/users)