Despite the widespread adoption of deep learning models, shallow machine learning (SML) algorithms are still used for image classification due to simplicity, interpretability and efficiency. This study aims to bridge this gap by investigating the robustness dynamics of SML techniques under more complex scenarios, such as adversarial perturbations and geometric transformation. Five popular classification algorithms, including k-nearest neighbour and support vector machines, were employed to build classification models. Methodology involves investigating the robustness of proposed methods, first, on original and corrupted data by utilising benchmark datasets across several image domains. To strengthen the investigation, the models were trained using a new low-rank representation (LRR) strategy. This hybrid model simultaneously addresses two key limitations in classical LRR models: overcoming the sequential learning process and effectively capturing both local and global data structures. By introducing a dual regularisation mechanism, it integrates a k-nearest neighbour graph to preserve local consistency, while a global low-rank constraint ensures coherent data representation. Experimental results reveal significant drops in accuracy of most SML methods, especially under adversarial attacks and geometric transformations, but LRR approach mitigates these effects to a notable extent, boosting performance across data variations. The results also show that the proposed method outperforms state-of-the-art LRR techniques in most experiments.

Compared to traditional electrodynamic loudspeakers, the parametric array loudspeaker (PAL) offers exceptional directivity for audio applications but suffers from significant nonlinear distortions due to its inherent intricate demodulation process. The Volterra filter-based approaches have been widely used to reduce these distortions, but the effectiveness is limited by its inverse filter's capability. Specifically, its$p$th-order inverse filter can only compensate for nonlinearities up to the$p$th order, while the higher-order nonlinearities it introduces continue to generate lower-order harmonics. In contrast, this paper introduces the modern deep learning methods for the first time to address nonlinear identification and compensation for PAL systems. Specifically, the WaveNet neural network, recognized for its success in audio nonlinear system modeling, is utilized to identify and compensate for distortions in a double sideband amplitude modulation-based PAL system. Experimental measurements from 250 Hz to 8 kHz demonstrate that our proposed approach significantly reduces both total harmonic distortion and intermodulation distortion of audio sound generated by PALs, achieving average reductions to 3.11% and 0.93%, respectively. This performance is notably superior to results obtained using the current state-of-the-art Volterra filter-based methods. Our work opens new possibilities for improving the sound reproduction performance of PALs.

A steerable parametric source is designed to steer an audio beam without mechanically rotating the source. To achieve this without the generation of grating lobes requires an ultrasonic array with interelement spacing that is less than half the wavelength of the carrier ultrasound because of the spatial Nyquist criterion. However, ultrasonic wavelengths are typically smaller than the size of an ultrasonic transducer and this generates grating lobes in the radiation pattern, which is known as the spatial aliasing effect. This work proposes a method to suppress sidelobes including these grating lobes by optimizing the position and weight coefficients of the array elements by using a sparse random array technique. This is achieved by using the peak sidelobe level as the objective function and the simulated annealing algorithm for the optimization. Both simulation and experimental results demonstrate the sidelobe level can be effectively suppressed when the average interelement spacing is two wavelengths. It is also found that Westervelt directivity has a significant effect on the spatial aliasing, because it serves as a spatial filter on the product directivity of the ultrasound. Accordingly, the sidelobe suppression performance deteriorates at low audio frequencies and high ultrasound frequencies where Westervelt directivity tends to be broader. However, this deterioration in performance can be addressed by increasing the number of the array elements.