Exploring the Power of Deep Learning for Seamless Background Audio Generation in Videos

In the realm of image and signal processing, "data" refers to any piece of information that can be transformed into a format that can be efficiently processed. It is projected that by 2025, there will be 180 zettabytes of accessible data, based on previous estimates. Video processing plays a critical role in signal processing, and video filters are a key element of this process. In essence, video filters are akin to signal processing filters, with the distinction being that the input and output signals in this case are video streams. This is analogous to image processing. The importance of audio cannot be overstated, as it enhances audience engagement, raises production value, and brings video subject matter to life, while also disseminating information to viewers. This research behind the paper aimed to build and compare numerous deep classifier models that can generate background audio for a given input video file using the greatest hits dataset.

I. INTRODUCTION

Videos are a visual medium that portrays knowledge, happenings, or artistic expressions and have become an indispensable part of our everyday routine [1]. They are omnipresent, found across diverse platforms such as social media, education resources, news broadcasting, and advertising campaigns. Videos have the power to influence our perception of the world and evoke robust emotional reactions. They possess the potential to be a forceful instrument for narrating stories, communicating complicated ideas, and exchanging experiences.