  Audio classification is the task of identifying the sound categories that are
associated with a given audio signal. This paper presents an investigation on
large-scale audio classification based on the recently released AudioSet
database. AudioSet comprises 2 millions of audio samples from YouTube, which
are human-annotated with 527 sound category labels. Audio classification
experiments with the balanced training set and the evaluation set of AudioSet
are carried out by applying different types of neural network models. The
classification performance and the model complexity of these models are
compared and analyzed. While the CNN models show better performance than MLP
and RNN, its model complexity is relatively high and undesirable for practical
use. We propose two different strategies that aim at constructing
low-dimensional embedding feature extractors and hence reducing the number of
model parameters. It is shown that the simplified CNN model has only 1/22 model
parameters of the original model, with only a slight degradation of
performance.
