Abstract
Acoustic scene recordings are often collected from a diverse range of cities. Most existing acoustic scene classification (ASC) approaches focus on identifying common acoustic scene patterns across cities to enhance generalization. In contrast, we hypothesize that city-specific environmental and cultural differences in acoustic features are beneficial for the ASC task. In this paper, we introduce City2Scene, a novel framework that leverages city features to improve ASC. City2Scene transfers the city-specific knowledge from city classification models to a scene classification model using knowledge distillation. We evaluated City2Scene on the DCASE Challenge Task 1 datasets, where each audio clip is annotated with both scene and city labels. Experimental results demonstrate that city features provide valuable information for classifying scenes. By distilling the city-specific knowledge, City2Scene effectively improves accuracy for various state-of-the-art ASC backbone models, including both CNNs and Transformers.
Abstract (translated)
声学场景录音通常是从不同城市的多样化环境中收集的。大多数现有的声学场景分类（ASC）方法侧重于识别跨城市之间普遍存在的声学场景模式，以增强泛化能力。相比之下，我们假设每个城市特有的环境和文化差异在声学特征中是有利于ASC任务的。本文介绍了City2Scene，这是一种新的框架，利用城市的特性来改进ASC。City2Scene通过知识蒸馏将特定于城市的知识从城市分类模型转移到场景分类模型上。 我们在DCASE挑战赛任务1数据集上评估了City2Scene，该数据集中每个音频片段都带有声学场景和城市的标签。实验结果表明，城市特征为声学场景的分类提供了有价值的信息。通过蒸馏出特定于城市的知识，City2Scene有效地提高了各种先进的ASC骨干模型（包括CNNs和Transformers）的准确性。
URL
https://arxiv.org/abs/2503.16862
