The creation of Personal Sound Zones (PSZ) is a recent application of digital signal processing that allows differentiating the sound intensity in neighboring regions of space (e.g. a 'bright' and a 'dark' zone). Given the impulse responses of the environment, digital filters can be designed in order to obtain an attenuation of the signal in the dark zone as a result of the superposition of the filtered IR coming from each loudspeaker. A neural optimization approach was recently shown to enable PSZ by designing digital FIR filters. In this work we propose an improvement of that neural optimization approach using a simpler neural network architecture. Furthermore we extend the method to the design of IIR filters, which is computationally more effective for a real-time implementation. The neural technique is compared with two state-of-the-art methods, analyzing the performance in terms of Acoustic Contrast. Experiments have been performed using a vehicle composed of standard loudspeakers and two speaker arrays, and show that the proposed approach achieves remarkable Acoustic Contrast without sacrificing audio quality.
Digital Filters Design for Personal Sound Zones: A Neural Approach / Pepe, G.; Gabrielli, L.; Squartini, S.; Tripodi, C.; Strozzi, N.. - 2022-:(2022), pp. 1-8. (Intervento presentato al convegno 2022 International Joint Conference on Neural Networks, IJCNN 2022 tenutosi a ita nel 2022) [10.1109/IJCNN55064.2022.9892571].