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A Machine Learning Approach to Hierarchical Categorization of Auditory Objects

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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.

JAES Volume 68 Issue 1/2 pp. 48-56; January 2020
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