  Improving the performance of on-device audio classification models remains a
challenge given the computational limits of the mobile environment. Many
studies leverage knowledge distillation to boost predictive performance by
transferring the knowledge from large models to on-device models. However, most
lack a mechanism to distill the essence of the temporal information, which is
crucial to audio classification tasks, or similar architecture is often
required. In this paper, we propose a new knowledge distillation method
designed to incorporate the temporal knowledge embedded in attention weights of
large transformer-based models into on-device models. Our distillation method
is applicable to various types of architectures, including the
non-attention-based architectures such as CNNs or RNNs, while retaining the
original network architecture during inference. Through extensive experiments
on both an audio event detection dataset and a noisy keyword spotting dataset,
we show that our proposed method improves the predictive performance across
diverse on-device architectures.

Sound;Machine Learning;Audio and Speech Processing