Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification. In this blog post we will demonstrate such an example by using the popular method of converting the audio signal into the frequency domain.
This blog post is a third of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML/DL project. The previous blog posts focused on image classification and hyperparameters optimization. In this blog post, we will show how using Torchaudio and Allegro Trains enables simple and efficient audio classification.

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