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.Index Terms-Array signal processing, personal audio, time domain optimization.
A superdirective array of audio drivers is described, which is compact compared with the acoustic wavelength over some of its frequency range. In order to minimize the overall sound power output, and hence reduce the excitation of the reverberant field when used in an enclosed space, the individual drivers are made directional by using phase shift enclosures. The motivating application for the array is the enhancement of sound from a television, in a particular region of space, to aid hearing impaired listeners. The design is initially investigated, using free-field simulations, by comparing the performance of 8 monopoles, 8 phase shift loudspeakers, and a double array of 16 monopoles, with a contrast maximization formulation. The construction and testing of an array of 8 drivers is then discussed, together with its measured response in an anechoic environment. The result of using acoustic contrast maximization is then compared with a least squares formulation, which demonstrates that the performance of the least squares solution can be made similar to that given by acoustic contrast maximization in this application, with a suitable choice of the target field.
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.
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