Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices
This work addresses robust and low-complexity acoustic scene classification for real-life edge device applications, but it is incremental as it builds on existing methods.
The paper tackles acoustic scene classification with mismatched recording devices by proposing a novel inception-residual-based network architecture and applying ensemble and channel reduction techniques, achieving 69.9% accuracy with 2.4M parameters on the DCASE 2020 dataset.
In this paper, we present a robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording. We first construct an ASC baseline system in which a novel inception-residual-based network architecture is proposed to deal with the mismatched recording device issue. To further improve the performance but still satisfy the low complexity model, we apply two techniques: ensemble of multiple spectrograms and channel reduction on the ASC baseline system. By conducting extensive experiments on the benchmark DCASE 2020 Task 1A Development dataset, we achieve the best model performing an accuracy of 69.9% and a low complexity of 2.4M trainable parameters, which is competitive to the state-of-the-art ASC systems and potential for real-life applications on edge devices.