  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.
