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.

This paper describes a novel Deep Learning method for the design of IIR parametric filters for automatic multipoint audio equalization, that is the task of improving the sound quality of a listening environment at multiple listening points employing multiple loudspeakers. The filters are designed to approximate the inverse of the RIR and achieve almost flat magnitude response. A simple and effective neural architecture, named BiasNet, is proposed to determine the IIR equalizer parameters. This novel architecture is conceived for optimization and, as such, is able to produce optimal IIR equalizer parameters at its output, after training, with no input required. In absence of input, the presence of learnable non-zero bias terms ensures that the network works properly. An output scaling method is used to obtain accurate tuning of the IIR filters center frequency, quality factor and gain. All layers involved in the proposed method are shown to be differentiable, allowing backpropagation to optimize the network weights and achieve, after a number of training iterations, the optimal output according to a given RIR. The parameters are optimized with respect to a loss function based on a spectral distance between the measured and desired magnitude response, and a regularization term is used to keep the same microphone-loudspeaker energy balance after equalization. Two experimental scenarios are employed, a room and a car cabin, with several loudspeakers. The performance of the proposed method improves over the baseline techniques and achieves an almost flat band at a lower computational cost.

A recent trend in car audio systems is the generation of Individual Listening Zones (ILZ), allowing to improve phone call privacy and reduce disturbance to other passengers, without wearing headphones or earpieces. This is generally achieved by using loudspeaker arrays. In this paper, we describe an approach to achieve ILZ exploiting general purpose car loudspeakers and processing the signal through carefully designed Finite Impulse Response (FIR) filters. We propose a deep neural network approach for the design of filters coefficients in order to obtain a so-called bright zone, where the signal is clearly heard, and a dark zone, where the signal is attenuated. Additionally, the frequency response in the bright zone is constrained to be as flat as possible. Numerical experiments were performed taking the impulse responses measured with either one binaural pair or three binaural pairs for each passenger. The results in terms of attenuation and flatness prove the viability of the approach.