Audio Enhancement from Multiple Crowdsourced Recordings: A Simple and Effective Baseline

TL;DR

This paper introduces a straightforward crowdsourced audio enhancement technique that cleans multiple noisy recordings from different sources and averages them to produce a clearer audio of the event, serving as a baseline for future research.

Contribution

It presents a simple, effective method for enhancing crowdsourced audio by removing local noise and combining signals, addressing a rarely studied scenario of uncorrelated noise from multiple devices.

Findings

Effective noise removal from multiple recordings

Improved audio quality through averaging cleaned signals

Establishes a new baseline for crowdsourced audio enhancement

Abstract

With the popularity of cellular phones, events are often recorded by multiple devices from different locations and shared on social media. Several different recordings could be found for many events. Such recordings are usually noisy, where noise for each device is local and unrelated to others. This case of multiple microphones at unknown locations, capturing local, uncorrelated noise, was rarely treated in the literature. In this work we propose a simple and effective crowdsourced audio enhancement method to remove local noises at each input audio signal. Then, averaging all cleaned source signals gives an improved audio of the event. We demonstrate the effectiveness of our method using synthetic audio signals, together with real-world recordings. This simple approach can set a new baseline for crowdsourced audio enhancement for more sophisticated methods which we hope will be developed by the research community.