Abstract
Acoustic Scene Classification (ASC) is a fundamental problem in computational audition, which seeks to classify environments based on the distinctive acoustic features. In the ASC task of the APSIPA ASC 2025 Grand Challenge, the organizers introduce a multimodal ASC task. Unlike traditional ASC systems that rely solely on audio inputs, this challenge provides additional textual information as inputs, including the location where the audio is recorded and the time of recording. In this paper, we present our proposed system for the ASC task in the APSIPA ASC 2025 Grand Challenge. Specifically, we propose a multimodal network, \textbf{ASCMamba}, which integrates audio and textual information for fine-grained acoustic scene understanding and effective multimodal ASC. The proposed ASCMamba employs a DenseEncoder to extract hierarchical spectral features from spectrograms, followed by a dual-path Mamba blocks that capture long-range temporal and frequency dependencies using Mamba-based state space models. In addition, we present a two-step pseudo-labeling mechanism to generate more reliable pseudo-labels. Results show that the proposed system outperforms all the participating teams and achieves a 6.2% improvement over the baseline. Code, model and pre-trained checkpoints are available at this https URL.
Abstract (translated)
声场景分类（ASC）是计算听觉学中的一个基本问题，旨在根据独特的声学特征对环境进行分类。在APSIPA ASC 2025大赛的ASC任务中，组织者引入了一个多模态ASC任务。与传统的仅依赖音频输入的传统ASC系统不同，此次挑战提供了额外的文字信息作为输入，包括录音的位置和时间。 本文介绍了我们为APSIPA ASC 2025大赛的ASC任务设计的系统。具体来说，我们提出了一种多模态网络——**ASCMamba**，该网络整合了音频与文本信息，用于细粒度声场景理解和有效的多模态ASC分类。提出的ASCMamba采用DenseEncoder从频谱图中提取分层频谱特征，并使用基于Mamba的双路径Mamba块捕捉长时延和频率依赖性。 此外，我们还提出了一种两步伪标签生成机制来产生更加可靠的伪标签。结果显示，我们的系统在所有参赛队伍中表现最优，并且相较于基线方法有6.2%的表现提升。代码、模型以及预训练的检查点可在以下链接获取：[此URL]（请将此处的"this https URL"替换为实际提供的网址）。
URL
https://arxiv.org/abs/2508.15632
