Object-based audio is an emerging representation for audio content, where content is represented in a reproduction-format-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 paper 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 audio-visual interfaces to support object-based capture and listener-tracked rendering, and incorporates a proposed component for objectification, that is, 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.

With the development of so-called next generation audio systems the question of evaluation of such immersive or object-based systems is of large interest for the industry. This paper presents the multiple stimulus ideal profile method for the practical assessment of next generation sound systems. The approach takes best practices from a number of different well-recognised methods as well as some novel ones from other industries. The paper presents the method as well as the initial results of the test using specially designed test items to investigate the characteristics of the method. Practical experiences in two labs with different listener groups and statistical analysis will be discussed in detail.

This paper reports the latest developments in ongoing research to establish a common lexicon of sound quality attributes agnostic to the application under study. It expands on earlier work with the definition of attributes for the evaluation of spatial sound reproduction. A panel of 12 specialized expert assessors was used to expand the lexicon to incorporate spatial sound characteristics. The discussions were based on the results of a semantic clustering of relevant quality attribute sets proposed in the literature. The updated lexicon is presented, with detailed definitions, scale labels and examples provided for the attributes. Further review and formal validation are required in future.

We present a novel dataset for multi-view video and spatial audio. An ensemble of ten musicians from the BBC Philharmonic Orchestra performed in the orchestra's rehearsal studio in Salford, UK, on 25th March 2014. This presented a controlled environment in which to capture a dataset that could be used to simulate a large event, whilst allowing control over the conditions and performance. The dataset consists of hundreds of video and audio clips captured during 18 takes of performances, using a broad range of professional-and consumer-grade equipment, up to 4K video and high-end spatial microphones. In addition to the audiovisual essence, sensor metadata has been captured, and ground truth annotations, in particular for temporal synchronization and spatial alignment, have been created. A part of the dataset has also been prepared for adaptive content streaming. The dataset is released under a Creative Commons Attribution Non-Commercial Share Alike license and hosted on a specifically adapted content management platform.