AES E-Library

AES E-Library

A deep learning approach to sound classification for film audio post-production

Audio post-production for film involves the manipulation of large amounts of audio data. There is a need for the automation of many organization tasks currently performed manually by sound engineers, such as grouping and renaming multiple audio recordings. Here, we present a method to classify such sound files in two categories, ambient recordings and single-source sounds. Automating these classification tasks requires a deep learning model capable of answering questions about the nature of each sound recording based on specific features. This study focuses on the relevant features for this type of audio classification and the design of one possible model. In addition, an evaluation of the model is presented, resulting in high accuracy, precision and recall values for audio classification.

Authors:
Affiliation:
AES Convention: Paper Number:
Publication Date:
Subject:
Permalink: http://www.aes.org/e-lib/browse.cfm?elib=20739

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location:

Start a discussion about this paper!


AES - Audio Engineering Society