Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models

2026-06-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel framework called Acoustic Prompting via Stage-wise Modulation enhances few-shot learning in Audio-Language Models (ALMs) by introducing trainable prompts directly into the audio encoder. While prior efforts focused on optimizing text prompts for ALMs to improve zero-shot audio classification, this new approach explores the untapped potential of learnable prompts within the audio processing pipeline. By capturing task-specific acoustic features through these audio-side prompts and integrating them with existing text-side prompt tuning methods, the framework significantly improves few-shot adaptation. Extensive experiments conducted across 11 diverse datasets demonstrate that this method, implemented as a plug-and-play module, consistently leads to performance improvements, effectively complementing text-only prompting by modulating the audio representation space.

Key takeaway

For Machine Learning Engineers developing Audio-Language Models, consider integrating audio-side prompt tuning to significantly enhance few-shot learning performance. Your current text-only prompting strategies can be effectively complemented by explicitly modulating the audio representation space with task-specific acoustic prompts. This plug-and-play approach, demonstrated across 11 datasets, offers a direct path to improved adaptation, making your ALMs more robust for new audio classification tasks. Explore the provided code to implement this dual-prompting strategy.

Key insights

Integrating trainable audio-side prompts with text-side prompt tuning significantly enhances few-shot adaptation in Audio-Language Models.

Principles

• Audio encoders benefit from task-specific prompts.
• Modulating audio representation complements text prompts.
• Combined audio and text prompts improve few-shot learning.

Method

Introduce trainable prompts into the audio encoder to capture task-specific acoustic features. Integrate this audio-side prompt learning as a plug-and-play module alongside existing text-side prompt tuning approaches.

In practice

• Apply audio-side prompts to ALM audio encoders.
• Combine audio and text prompt tuning for few-shot tasks.
• Utilize the provided code for implementation.

Topics

• Audio-Language Models
• Few-Shot Learning
• Acoustic Prompting
• Prompt Tuning
• Audio Encoders
• Audio Classification

Code references

• hyebin-c/aspl

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.