Amplitude panning produces interaural time difference (ITD) cues that help localize images in directions between loudspeakers. However, if the panning gains are static, then the ITD cues produced in this way vary inconsistently as the listener's head rotates, compared with a real source, and so the dynamic ITD cues are inaccurate. This effect destabilizes the perception of the image and overall scene, and is worse for loudspeakers that are more widely spaced relative to the listener. Based on a simple head model that is accurate in the low-frequency ITD regime, the ITD is calculated for a general field, including those produced by panning. A simple formula is derived relating head orientation, image direction, and a field description vector. Panning functions are then found that compensate for head orientation and are valid for any image direction. For the special case when the listener is facing the image, the functions are equivalent to vector base amplitude panning. The performance is first assessed objectively using measured binaural responses, rather than the simple head model. Subjective comparison is then made with pre-existing listening tests and new listening tests in which the listener's head is tracked to control the panning gains in real-time. These show that images can be stabilized as predicted, and, furthermore, that with the same panning functions, images can be produced in all directions using two loudspeakers placed in front.

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.

This paper describes a time domain design method for calculating the coefficients of FIR filters used to drive a loudspeaker array for personal audio. A motivating application is to boost the television audio in a certain spatial region with the aim of increasing the speech intelligibility of the hearing impaired. As the array is of small size, superdirective beamforming is applied to increase the low and mid frequency directional performance of the radiator. The filters for such arrays have previously been designed one frequency at a time, leading to non-causal filters that require a modeling delay for real time implementation. By posing the filter optimization in the time domain, the filter responses can be causally constrained, and the optimization is performed once for all frequencies. This paper considers the performance of such filters by carrying out off-line simulations, firstly using the impulse responses of point sources in the free field, and then with the measured responses of a loudspeaker array in an anechoic chamber. The simulation results show how the time domain optimization allows the creation of filters with either a low order or a low modeling delay.