Reducing Model Complexity for DNN Based Large-Scale Audio Classification
This work addresses the issue of impractical model size for real-world audio classification applications, representing an incremental improvement in efficiency.
The paper tackled the problem of high model complexity in deep neural networks for large-scale audio classification using the AudioSet database, achieving a simplified CNN model with only 1/22 of the original parameters while maintaining near-original performance.
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.