  Acoustic Event Classification (AEC) has been widely used in devices such as
smart speakers and mobile phones for home safety or accessibility support. As
AEC models run on more and more devices with diverse computation resource
constraints, it became increasingly expensive to develop models that are tuned
to achieve optimal accuracy/computation trade-off for each given computation
resource constraint. In this paper, we introduce a Once-For-All (OFA) Neural
Architecture Search (NAS) framework for AEC. Specifically, we first train a
weight-sharing supernet that supports different model architectures, followed
by automatically searching for a model given specific computational resource
constraints. Our experimental results showed that by just training once, the
resulting model from NAS significantly outperforms both models trained
individually from scratch and knowledge distillation (25.4% and 7.3% relative
improvement). We also found that the benefit of weight-sharing supernet
training of ultra-small models comes not only from searching but from
optimization.
