ASCDomain: Domain Invariant Device-Adversarial Isotropic Knowledge Distillation Convolutional Neural Architecture

Abstract

Recent advancements in deep learning for Acoustic Scene Classification (ASC) have significantly improved the ability to discern and categorize complex soundscapes. Nonetheless, the real-world application of these technologies presents notable challenges, particularly regarding computational demands and adaptability to diverse environmental conditions. To address key issues such as recording device mismatch, strict memory and complexity constraints, and the limited availability of labeled data, we introduce ASCDomain - an innovative framework designed for efficient and adaptable acoustic scene analysis. Our approach combines a teacher-student knowledge distillation mechanism, where an ensemble of high-performance models serves as the teacher, enhancing the training process. This is further supported by a domain adversarial neural network that ensures robust domain adaptation. By integrating a compact isotropic neural network as the student, our system minimizes computational requirements while reducing the dependency on extensive labeled datasets. Validated on the TAU Urban Acoustic Scenes 2022 Mobile dataset, ASCDomain demonstrates leading-edge performance. The code is available at https://github.com/hubtru/ASCDomain.