# ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification

arXiv: https://arxiv.org/abs/2508.15632 · HTML: https://mlsanity.com/paper/2508.15632/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Speech and audio SSMs](https://mlsanity.com/ssm/speech-and-audio-ssms/)

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [CochlScene: Acquisition of acoustic scene data using crowdsourcing](https://mlsanity.com/paper/2211.02289/) (2022)
- [Acoustic Scene Classification](https://mlsanity.com/paper/1411.3715/) (2014)
- [DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs](https://mlsanity.com/paper/2403.19588/) (2024)
- [A Two-Stage Approach to Device-Robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.01447/) (2020)
- [Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2402.02694/) (2024)
- [Exploring Text-Queried Sound Event Detection with Audio Source Separation](https://mlsanity.com/paper/2409.13292/) (2024)
- [A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition](https://mlsanity.com/paper/2203.08350/) (2022)

## Abstract (arXiv)

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, 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 https://github.com/S-Orion/ASCMamba.git.