Applications of Deep Learning: Sequence Modelling in Industrial Sound Analytics

Abstract

In today's world of data and information, deep learning has proven to be the ace in data science applications. While we hear much of deep learning concerning computer vision, NLP (Natural Language Processing), audio analysis and its usefulness to ML have been recognised in the past decade. Audio data analysis analyses and understands audio signals captured by digital devices such as microphones. It has widespread applications, including automatic speech recognition (ASR), digital signal processing, medical diagnostics, music classification, tagging and generation. Factory machinery is prone to malfunction or breakdown, resulting in a significant loss of time, money and resources. So, there is a need for an efficient and affordable solution to this problem. So, there is a rising interest in building deep learning models for monitoring machines. The Industrial Internet of Things (IoT) and data-driven techniques have revolutionised the manufacturing industry. Many new and innovative approaches have been attempted to build algorithms to monitor machinery. Many new and innovative approaches have been attempted to build algorithms to monitor machinery. Sound Classification is one of the most popular applications of Deep Learning. It involves classifying sounds based on various frameworks like music genre prediction, urban sound classification, identification of speaker or tone from sound clips. It has huge applications, especially in manufacturing industries, like predicting machine failures, spotting faulty pieces of machinery.