# Basics

Series 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. [Automatic Speech Recognition](https://towardsdatascience.com/audio-deep-learning-made-simple-automatic-speech-recognition-asr-how-it-works-716cfce4c706) *(Speech-to-Text algorithm and architecture, using CTC Loss and Decoding for aligning sequences.)*
6. [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)*


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