State Space and Self-Attention Collaborative Network with Feature Aggregation for DOA Estimation

TL;DR

The paper introduces FA-Stateformer, a novel neural network architecture combining feature aggregation, state space modeling, and self-attention for improved and efficient sound source DOA estimation.

Contribution

It proposes a new collaborative network integrating feature aggregation, a lightweight Conformer, temporal shift, and bidirectional Mamba modules for enhanced temporal modeling and efficiency.

Findings

Outperforms conventional architectures in accuracy.

Achieves better computational efficiency.

Demonstrates robustness across various scenarios.

Abstract

Accurate direction-of-arrival (DOA) estimation for sound sources is challenging due to the continuous changes in acoustic characteristics across time and frequency. In such scenarios, accurate localization relies on the ability to aggregate relevant features and model temporal dependencies effectively. In time series modeling, achieving a balance between model performance and computational efficiency remains a significant challenge. To address this, we propose FA-Stateformer, a state space and self-attention collaborative network with feature aggregation. The proposed network first employs a feature aggregation module to enhance informative features across both temporal and spectral dimensions. This is followed by a lightweight Conformer architecture inspired by the squeeze-and-excitation mechanism, where the feedforward layers are compressed to reduce redundancy and parameter overhead. Additionally, a temporal shift mechanism is incorporated to expand the receptive field of convolutional layers while maintaining a compact kernel size. To further enhance sequence modeling capabilities, a bidirectional Mamba module is introduced, enabling efficient state-space-based representation of temporal dependencies in both forward and backward directions. The remaining self-attention layers are combined with the Mamba blocks, forming a collaborative modeling framework that achieves a balance between representation capacity and computational efficiency. Extensive experiments demonstrate that FA-Stateformer achieves superior performance and efficiency compared to conventional architectures.