基于深度学习的参量阵扬声器非线性失真识别与补偿方法
Deep Learning-Based Approach for Identification and Compensation of Nonlinear Distortions in Parametric Array Loudspeakers
AI总结：
本文首次引入深度学习方法，利用WaveNet前馈变体识别并补偿参量阵扬声器（PAL）的非线性失真，实验表明其总谐波失真和互调失真平均降至4.55%和2.47%，显著优于传统Volterra滤波器方法。
摘要：
与传统电动扬声器相比，参量阵扬声器（PAL）为音频应用提供了卓越的指向性，但由于其固有的复杂解调过程而存在严重的非线性失真。基于Volterra滤波器的方法已被广泛用于减少这些失真，但其有效性受限于其逆滤波器的能力。具体而言，其p阶逆滤波器只能补偿最高至p阶的非线性，而其引入的高阶非线性会继续产生低阶谐波。相比之下，本文首次引入现代深度学习方法来解决PAL系统的非线性识别与补偿问题。具体而言，利用在音频非线性系统建模中取得成功的WaveNet神经网络前馈变体，来识别和补偿基于双边带幅度调制的PAL系统中的失真。从250 Hz到8 kHz的实验测量表明，我们提出的方法显著降低了PAL产生的音频的总谐波失真和互调失真，分别实现了平均降低至4.55%和2.47%的效果。这一性能明显优于当前最先进的基于Volterra滤波器方法获得的结果。我们的工作为提升PAL的声音再现性能开辟了新的可能性。
英文摘要：
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.
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