Director of Research (if dissertation) or Advisor (if thesis)
Smaragdis, Paris
Department of Study
Electrical & Computer Eng
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
signal processing
speech enhancement
audio processing
audio editor
denoising
dereverberation
equalization
acoustic matching
digital audio work station
example-based editing
machine learning
Abstract
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.
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