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Object-based audio is gaining momentum as a means for future audio content to be more immersive, interactive, and accessible. Recent standardization developments make recommendations for object formats; however, the capture, production, and reproduction of reverberation is an open issue. In this paper parametric approaches for capturing, representing, editing, and rendering reverberation over a 3D spatial audio system are reviewed. A framework is proposed for a Reverberant Spatial Audio Object (RSAO), which synthesizes reverberation inside an audio object renderer. An implementation example of an object scheme utilizing the RSAO framework is provided, and supported with listening test results, showing that: the approach correctly retains the sense of room size compared to a convolved reference; editing RSAO parameters can alter the perceived room size and source distance; and, format-agnostic rendering can be exploited to alter listener envelopment.

Object-based audio is an emerging representation for audio content, where content is represented in a reproductionformat-agnostic way and thus produced once for consumption on many different kinds of devices. This affords new opportunities for immersive, personalized, and interactive listening experiences. This article introduces an end-to-end object-based spatial audio pipeline, from sound recording to listening. A high-level system architecture is proposed, which includes novel audiovisual interfaces to support object-based capture and listenertracked rendering, and incorporates a proposed component for objectification, i.e., recording content directly into an object-based form. Text-based and extensible metadata enable communication between the system components. An open architecture for object rendering is also proposed. The system's capabilities are evaluated in two parts. First, listener-tracked reproduction of metadata automatically estimated from two moving talkers is evaluated using an objective binaural localization model. Second, object-based scene capture with audio extracted using blind source separation (to remix between two talkers) and beamforming (to remix a recording of a jazz group), is evaluated with perceptually-motivated objective and subjective experiments. These experiments demonstrate that the novel components of the system add capabilities beyond the state of the art. Finally, we discuss challenges and future perspectives for object-based audio workflows.

This paper presents a series of experiments to determine a categorization framework for broadcast audio objects. Object-based audio is becoming an evermore important paradigm for the representation of complex sound scenes. However, there is a lack of knowledge regarding object level perception and cognitive processing of complex broadcast audio scenes. As categorization is a fundamental strategy in reducing cognitive load, knowledge of the categories utilized by listeners in the perception of complex scenes will be beneficial to the development of perceptually based representations and rendering strategies for object-based audio. In this study expert and non-expert listeners took part in a free card sorting task using audio objects from a variety of different types of program material. Hierarchical agglomerative clustering suggests that there are seven general categories, which relate to sounds indicating actions and movement, continuous and transient background sound, clear speech, non-diegetic music and effects, sounds indicating the presence of people, and prominent attention grabbing transient sounds. A three-dimensional perceptual space calculated via multidimensional scaling suggests that these categories vary along dimensions related to the semantic content of the objects, the temporal extent of the objects, and whether the object indicates the presence of people.