Fast technique for auralization generation using artificial neural networks
DOI:
https://doi.org/10.55753/aev.v33e50.84Keywords:
acoustic virtual reality, room auralization, BRIRs generation, artificial neural networks, room acoustic simulation, articulation indexAbstract
The main goal in development of numerical techniques in acoustic virtual reality systems and production of reliable auralizations is to reduce the computational cost and, at the same time, to guarantee the sound quality. In this paper, a new technique for modeling head-related transfer functions are presented. Artificial neural networks of the radial basis functions type are used. A set of these networks is trained and tested to cover the entire auditory space around the head. Each neural network for a given direction has as input the spectrum of the sound ray that reaches the receiver and, as output, the filtered head-related impulse response, for the corresponding direction working directly in the time domain and circumventing the need for convolutions with a computational cost reduction of 90%. The proposed technique is compared with the convolution method in both the time and frequency domain. The results show the efficiency of the proposed technique, with correlation values very close to one. To validate the result, preliminary tests using articulation indices to compare speech intelligibility in an actual and virtual room were conducted, with fully satisfactory results.
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