Auralization generated by modeling HRIRs with artificial neural networks and its validation using articulation tests

Abstract

The usual method to generate an auralization is to simulate the acoustic field with a computer code and to convolve each wavefront that arrives to the receiver with the corresponding measured head-related impulse response (HRIR) direction, to obtain the binaural room impulse responses (BRIRs) for selected positions. In this work, this technique is replaced by a procedure where the HRIRs are modeled by a set of artificial neural networks, with significant computational gain. The type of network and its architecture is briefly described and a comparison between measured and modeled responses — both in time and frequency domains — are presented for several directions covering the sphere around the head. Furthermore, since graphs do not tell all when auralizations are the concern, a virtual auralization assessment is performed. In this work, computer modeling auralizations are validated through articulation tests. Three articulation scores are computed. The first one is obtained in the actual room itself. The second one is obtained from measuring the BRIRs and convolving them with anechoic speech signals. The third one result from the room’s numerical simulation, generating as output the virtual BRIRs. These are then convolved with the same anechoic speech signals. The three articulation scores are then compared, and this is done in four different conditions. To investigate the influence of background noise level in the error among the compared articulation scores, pink noise with two distinct levels is introduced in one of the rooms, both in the actual and virtual tests. Finally, one strongly reverberant room is considered in other to evaluate if, in this condition of low intelligibility, the auralization is still trustworthy. In all cases, the results show that differences lower than 3.5% among actual and virtual articulation scores were found. The main conclusions are that the virtual auralizations obtained with the room simulator using artificial neural networks to model the head-related impulse responses are successful and that articulation scores are a reliable metric to validate auralizations.