  Acoustic scene classification (ASC) is highly important in the real world.
Recently, deep learning-based methods have been widely employed for acoustic
scene classification. However, these methods are currently not lightweight
enough as well as their performance is not satisfactory. To solve these
problems, we propose a deep space separable distillation network. Firstly, the
network performs high-low frequency decomposition on the log-mel spectrogram,
significantly reducing computational complexity while maintaining model
performance. Secondly, we specially design three lightweight operators for ASC,
including Separable Convolution (SC), Orthonormal Separable Convolution (OSC),
and Separable Partial Convolution (SPC). These operators exhibit highly
efficient feature extraction capabilities in acoustic scene classification
tasks. The experimental results demonstrate that the proposed method achieves a
performance gain of 9.8% compared to the currently popular deep learning
methods, while also having smaller parameter count and computational
complexity.
