Speaker protection algorithm is to leverage the playback signal properties to prevent over excursion while maintaining maximum loudness, especially for the mobile phone with tiny loudspeakers. This paper proposes efficient DL solutions to accurately model and predict the nonlinear excursion, which is challenging for conventional solutions. Firstly, we build the experiment and pre-processing pipeline, where the feedback current and voltage are sampled as input, and laser is employed to measure the excursion as ground truth. Secondly, one FFTNet model is proposed to explore the dominant low-frequency and other unknown harmonics, and compares to a baseline ConvNet model. In addition, BN re-estimation is designed to explore the online adaptation; and INT8 quantization based on AI Model efficiency toolkit (AIMET\footnote{AIMET is a product of Qualcomm Innovation Center, Inc.}) is applied to further reduce the complexity. The proposed algorithm is verified in two speakers and 3 typical deployment scenarios, and $>$99\% residual DC is less than 0.1 mm, much better than traditional solutions.

翻译：扬声器保护算法旨在利用播放信号特性在保持最大音量的同时防止过度位移，尤其适用于配备小型扬声器的手机。本文提出高效的深度学习解决方案，以精确建模和预测非线性位移，这是传统方法难以解决的问题。首先，我们搭建实验和预处理流程，将反馈电流和电压作为输入采样，并采用激光测量位移作为真实值。其次，提出一种FFTNet模型来探索主要的低频成分及其他未知谐波，并与基线ConvNet模型进行比较。此外，设计了BN重估计以实现在线自适应；基于AI模型效率工具包（AIMET）的INT8量化被进一步应用于降低计算复杂度。所提算法在两个扬声器和三种典型部署场景中验证，99%以上的残余直流分量小于0.1毫米，性能显著优于传统解决方案。