Just like classifying hand-written digits using the MNIST dataset is considered a ‘Hello World”-type problem for Computer Vision, we can think of this application as the introductory problem for audio deep learning.
Beam Search (Algorithm commonly used by Speech-to-Text and NLP applications to enhance predictions). Automatic Speech Recognition (Speech-to-Text algorithm and architecture, using CTC Loss and Decoding for aligning sequences.). Data Preparation and Augmentation (Enhance Spectrograms features for optimal performance by hyper-parameter tuning and data augmentation). What are Mel Spectrograms and how to generate them) Why Mel Spectrograms perform better (Processing audio data in Python. What are Spectrograms and why they are all-important.) What problems is audio deep learning solving in our daily lives. 
State-of-the-Art Techniques (What is sound and how it is digitized. They explore other fascinating topics in this space including how we prepare audio data for deep learning, why we use Mel Spectrograms for deep learning models and how they are generated and optimized. I have a few more articles in my audio deep learning series that you might find useful. 
My goal throughout will be to understand not just how something works but why it works that way. In this article, we will walk through a simple demo application so as to understand the approach used to solve such audio classification problems. classifying music clips to identify the genre of the music, or classifying short utterances by a set of speakers to identify the speaker based on the voice. This type of problem can be applied to many practical scenarios e.g. It involves learning to classify sounds and to predict the category of that sound.
Sound Classification is one of the most widely used applications in Audio Deep Learning.
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