Example-based audio editing

Traditionally, audio recordings are edited through digital audio workstations (DAWs), which give users access to different tools and parameters through a graphical user interface (GUI) without prior knowledge in coding or signal processing. The complexity of working with DAWs and the undeniable need for strong listening skills have made audio editing unpopular among novice users and time consuming for professionals. We propose an intelligent audio editor (EBAE) that automates major audio editing routines with the use of an example sound and efficiently provides users with high-quality results. EBAE first extracts meaningful information from an example sound that already contains the desired effects and then applies them to a desired recording by employing signal processing and machine learning techniques.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Anushiravani, Ramin

Contributors dc:contributor

• Smaragdis, Paris

• signal processing
• speech enhancement
• audio processing
• audio editor
• denoising
• dereverberation
• equalization
• acoustic matching
• digital audio work station
• example-based editing
• machine learning

Statement dc:rights

• Copyright 2016 Ramin Anushiravani

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/95331

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/95331