SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios

2026-07-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

SelectTSL is a novel end-to-end architecture designed for prompt-guided selective target sound localization in complex, multi-source acoustic environments. This system addresses a critical gap where existing deep learning methods either localize all active sound sources indiscriminately or extract target sources without preserving crucial multichannel spatial information. SelectTSL employs a target-aware selective localization strategy, featuring a Prompt-Guided Selective Attention Module (PGSA) that generates prompt-informed embeddings. These embeddings then guide an inter-channel phase difference (IPD) enhancer to refine raw phase cues, which are fused with target magnitudes. This integrated approach jointly estimates the Direction of Arrival (DoA) and target-source cardinality, effectively focusing on user-specified target spatial cues. Extensive experiments on both synthetic and real-world recordings demonstrate SelectTSL's consistent superior performance over baselines and robust generalization capabilities.

Key takeaway

For Machine Learning Engineers developing advanced audio processing systems, SelectTSL presents a significant advancement in selective target sound localization. If your current systems struggle with isolating and localizing specific sounds in complex, multi-source environments, you should investigate integrating prompt-guided selective attention and inter-channel phase difference (IPD) refinement. This approach can dramatically improve the accuracy of Direction of Arrival (DoA) estimation for user-specified targets, enhancing performance in applications requiring precise auditory focus, such as robotics or smart home devices.

Key insights

SelectTSL enables selective sound localization by fusing prompt-guided attention with inter-channel phase difference refinement.

Principles

• Selective attention improves localization in multi-source scenes.
• Fusing magnitude and refined phase cues enhances DoA estimation.
• Jointly estimating DoA and cardinality handles dynamic targets.

Method

SelectTSL uses a PGSA for prompt-informed embeddings, guiding an IPD enhancer to refine phase cues. These fuse with target magnitudes to estimate DoA and target-source cardinality.

In practice

• Implement prompt-guided attention for complex audio scenes.
• Integrate IPD refinement with magnitude for precise DoA.
• Develop systems that adapt to varying target source counts.

Topics

• Sound Source Localization
• Target Sound Localization
• Prompt-Guided Attention
• Direction of Arrival
• Acoustic Scene Analysis
• Deep Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.