Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block
This work addresses the need for efficient audio classification models for applications like environmental sound and music genre recognition, offering a more practical alternative to resource-intensive methods.
The paper tackles the problem of high computational costs and data requirements in audio classification by proposing a lightweight network called PIPMN, which achieves 96% accuracy on UrbanSound8K and 93.2% on GTAZN with only 1 million parameters and no data augmentation or transfer.
Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPMN