With the advent of new audio delivery technologies, object-based audio conceives of the audio content as being created at the delivery end of the chain. The concept of object-based audio envisages content delivery not via a fixed mix but as a series of auditory objects that can then be controlled either by consumers or by content creators and providers via the accompanying metadata. The proliferation of a variety of consumption modes (stereo headphones, home cinema systems, "hearables"), media formats (mp3, CD, video and audio streaming) and content types (gaming, music, drama, and current affairs broadcasting) has given rise to a complicated landscape where content must often be adapted for multiple end-use scenarios. Such a separation of audio assets facilitates the concept of Variable Asset Compression, where the most important elements from a perceptual standpoint are prioritized before others. In order to implement such a system however, insight is first required into what objects are most important, and how this importance changes over time. This research investigates the first of these questions, the hierarchical classification of isolated auditory objects using machine learning techniques. The results suggest that audio object hierarchies can be successfully modeled.
https://www.aes.org/e-lib/browse.cfm?elib=20717
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
Start a discussion about this paper!
