Conventional loudspeakers generate sound through the vibration of a diaphragm, attached to a rigid frame through elastic suspensions. Although such construction is satisfactory for sound diffusion in steady environments, it is likely to fail in harsh conditions, which is often the case for active noise control applications. Plasma-based actuators appear to be a promising alternative since they do not involve any fragile moving parts. In this paper, a positive corona discharge actuator in a wire-to-mesh geometry is proposed in the perspective of active noise control applications, as it is capable of generating sufficient sound pressure levels with limited signal distortion. The study introduces analytical and numerical models aiming at characterizing the sound field generated by the corona discharge actuator. The numerical simulation can facilitate the designing of such transducers. The acoustic power of the experimental prototype is increased through the optimization of emitter wires arrangement. The comparison of analytical model and numerical simulation with the experiment is presented. The analytical model successively describes the low frequency sound pressure field, while the numerical simulation is valid in the broader frequency range.

The combination of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) has already resulted in researchers advancing in quite a few real world applications but it is in control that this alliance yields such appreciable benefit. The paper reports a Radial Basis Function (RBF) network training technique which joins together global strategy of GAs and a local adjusting procedure typical for RBF networks. While activation function window centres and widths are processed via a “slow” numeric GA, output‐layer neurone synaptic weights are defined by a “fast” analytical method. The technique allows to minimize not only the network hidden-layer size but also the pattern set required for training the adequate dynamical object neuroemulator.

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Controlling audible sound requires inherently broadband and subwavelength acoustic solutions, which are to date, crucially missing. This includes current noise absorption methods, such as porous materials or acoustic resonators, which are typically inefficient below 1 kHz, or fundamentally narrowband. Here, we solve this vexing issue by introducing the concept of plasmacoustic metalayers. We demonstrate that the dynamics of small layers of air plasma can be controlled to interact with sound in an ultrabroadband way and over deep-subwavelength distances. Exploiting the unique physics of plasmacoustic metalayers, we experimentally demonstrate perfect sound absorption and tunable acoustic reflection over two frequency decades, from several Hz to the kHz range, with transparent plasma layers of thicknesses down to λ/1000. Such bandwidth and compactness are required in a variety of applications, including noise control, audio-engineering, room acoustics, imaging and metamaterial design.

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