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This study presents a new technique for creating spatial sounds based on a convolution processor. The main objective of this research was to propose a new method for generating a set of impulse responses that guarantee a realistic spatial experience based on the fusion of amplitude data acquired from an omnidirectional microphone and directional data acquired from an intensity probe. The advantages of the proposed approach are its versatility and easy adaptation to playback in a variety of multi-speaker systems, as well as a reduction in the amount of data, thereby simplifying the measurement procedure required to create any set of channel responses at the post-production stage. This paper describes the concept behind the method, the data acquisition method, and the signal processing algorithm required to generate any number of high-quality channel impulse responses. Experimental results are presented to confirm the suitability of the proposed solution by comparing the results obtained for a traditional surround 5.1 recording system and the proposed approach. This study aims to highlight the potential of intensity impulse responses in the audio recording and virtual reality industries. [ABSTRACT FROM AUTHOR]

Abstract Room impulse responses (RIRs) are used in several applications, such as augmented reality and virtual reality. These applications require a large number of RIRs to be convolved with audio, under strict latency constraints. In this paper, we consider the compression of RIRs, in conjunction with fast time-domain convolution. We consider three different methods of RIR approximation for the purpose of RIR compression and compare them to state-of-the-art compression. The methods are evaluated using several standard objective quality measures, both channel-based and signal-based. We also propose a novel low-rank-based algorithm for fast time-domain convolution and show how the convolution can be carried out without the need to decompress the RIR. Numerical simulations are performed using RIRs of different lengths, recorded in three different rooms. It is shown that compression using low-rank approximation is a very compelling option to the state-of-the-art Opus compression, as it performs as well or better than on all but one considered measure, with the added benefit of being amenable to fast time-domain convolution.

Room impulse responses (RIRs) are used in several applications, such as augmented reality and virtual reality. These applications require a large number of RIRs to be convolved with audio, under strict latency constraints. In this paper, we consider the compression of RIRs, in conjunction with fast time-domain convolution. We consider three different methods of RIR approximation for the purpose of RIR compression and compare them to state-of-the-art compression. The methods are evaluated using several standard objective quality measures, both channel-based and signal-based. We also propose a novel low-rank-based algorithm for fast time-domain convolution and show how the convolution can be carried out without the need to decompress the RIR. Numerical simulations are performed using RIRs of different lengths, recorded in three different rooms. It is shown that compression using low-rank approximation is a very compelling option to the state-of-the-art Opus compression, as it performs as well or better than on all but one considered measure, with the added benefit of being amenable to fast time-domain convolution. [ABSTRACT FROM AUTHOR]

This study presents a new technique for creating spatial sounds based on a convolution processor. The main objective of this research was to propose a new method for generating a set of impulse responses that guarantee a realistic spatial experience based on the fusion of amplitude data acquired from an omnidirectional microphone and directional data acquired from an intensity probe. The advantages of the proposed approach are its versatility and easy adaptation to playback in a variety of multi-speaker systems, as well as a reduction in the amount of data, thereby simplifying the measurement procedure required to create any set of channel responses at the post-production stage. This paper describes the concept behind the method, the data acquisition method, and the signal processing algorithm required to generate any number of high-quality channel impulse responses. Experimental results are presented to confirm the suitability of the proposed solution by comparing the results obtained for a traditional surround 5.1 recording system and the proposed approach. This study aims to highlight the potential of intensity impulse responses in the audio recording and virtual reality industries.

The room impulse response (RIR) describes the response of a room to an acoustic excitation signal and models the acoustic channel between a point source and receiver. RIRs are used in a wide range of applications, e.g., virtual reality. In such an application, the availability of closely spaced RIRs and the capability to achieve low latency are imperative to provide an immersive experience. However, representing a complete acoustic environment using a fine grid of RIRs is prohibitive from a storage point of view and without exploiting spatial proximity, acoustic rendering becomes computationally expensive. We therefore propose two methods for the joint compression of multiple RIRs, based on the generalized low-rank approximation of matrices (GLRAM), for the purpose of efficiently storing RIRs and allowing for low-latency convolution. We show how one of the components of the GLRAM decomposition is virtually invariant to the change of position of the source throughout the room and how this can be exploited in the modeling and convolution. In simulations we show how this offers high compression, with less quality degradation than comparable benchmark methods.