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

Shiran Aziz, Yossi Adi, Shmuel Peleg

Research output: Contribution to journalConference articlepeer-review

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. Code, dataset, and models are available.

Original languageEnglish
Pages (from-to)3355-3359
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
DOIs
StatePublished - 2024
Event25th Interspeech Conferece 2024 - Kos Island, Greece
Duration: 1 Sep 20245 Sep 2024

Keywords

  • Audio enhancement
  • Crowdsourced denoising
  • Time-frequency filtering
  • User-Generated recordings

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Cite this