In the previous article, we started our discussion about audio signals; we saw how we can interpret and visualize them using Librosa python library. We also learned how to extract necessary features from a sound/audio file. We concluded the previous article by building an Artificial Neural Network(ANN) for the music genre classification. In this article, we are going to build a Convolutional Neural Network for music genre classification. Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure.

While much of the literature and buzz on deep learning concerns computer vision and natural language processing(NLP), audio analysis -- a field that includes automatic speech recognition(ASR), digital signal processing, and music classification, tagging, and generation -- is a growing subdomain of deep learning applications. Some of the most popular and widespread machine learning systems, virtual assistants Alexa, Siri, and Google Home, are largely products built atop models that can extract information from audio signals. Audio data analysis is about analyzing and understanding audio signals captured by digital devices, with numerous applications in the enterprise, healthcare, productivity, and smart cities. Applications include customer satisfaction analysis from customer support calls, media content analysis and retrieval, medical diagnostic aids and patient monitoring, assistive technologies for people with hearing impairments, and audio analysis for public safety. In the first part of this article series, we will talk about all you need to know before getting started with the audio data analysis and extract necessary features from a sound/audio file. We will also build an Artificial Neural Network(ANN) for the music genre classification.

When you get started with data science, you start simple. You go through simple projects like Loan Prediction problem or Big Mart Sales Prediction. These problems have structured data arranged neatly in a tabular format. In other words, you are spoon-fed the hardest part in data science pipeline. The datasets in real life are much more complex.