Series 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. Automatic Speech Recognition (Speech-to-Text algorithm and architecture, using CTC Loss and Decoding for aligning sequences.)

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

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