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 pth-order inverse filter can only compensate for nonlinearities up to the pth 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, a feedforward variant of 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 4.55% and 2.47%, 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.

翻译：与传统电动扬声器相比，参量阵扬声器（PAL）在音频应用中具有卓越的方向性，但由于其固有的复杂解调过程，存在显著的非线性失真。基于Volterra滤波器的方法已被广泛用于减少这些失真，但其效果受限于其逆滤波器的能力。具体而言，其p阶逆滤波器仅能补偿高达p阶的非线性，而其所引入的高阶非线性仍会继续产生低次谐波。相比之下，本文首次引入现代深度学习方法来解决PAL系统的非线性辨识与补偿问题。具体而言，采用在音频非线性系统建模中取得成功的WaveNet神经网络的前馈变体，对基于双边带幅度调制的PAL系统中的失真进行辨识与补偿。在250 Hz至8 kHz范围内的实验测量表明，我们提出的方法显著降低了PAL产生的音频信号的总谐波失真和互调失真，分别平均降至4.55%和2.47%。该性能明显优于使用当前最先进的基于Volterra滤波器方法所获得的结果。我们的工作为提升PAL的声音重放性能开辟了新的可能性。