Query-guided Audio Separation Extracts Sounds Using Text Descriptions.

A hierarchical framework, HSM-TSS, effectively separates target audio sources from complex scenes using text queries. The system decouples semantic-guided feature separation from acoustic reconstruction, employing a dual-stage mechanism and pretrained encoders for efficient cross-modal learning and superior semantic consistency with minimal labelled data.

The extraction of specific sounds from complex audio mixtures – a task known as audio source separation – is becoming increasingly refined through the incorporation of natural language queries. Researchers are now able to isolate or remove sounds based on textual descriptions, moving beyond simple identification of individual sources. A new approach, detailed in the article ‘Text-Queried Audio Source Separation via Hierarchical Modeling’ by Xinlei Yin, Xiulian Peng, Xue Jiang, Zhiwei Xiong, and Yan Lu, addresses key challenges in aligning audio with textual instructions and achieving effective separation with limited labelled data. Their work introduces a hierarchical framework that decomposes the separation process into stages focusing on global and local semantic features, coupled with an instruction pipeline for flexible sound manipulation.

A novel hierarchical framework, HSM-TSS, enhances audio source separation using textual instructions. Current techniques often struggle to align acoustic features with textual descriptions and demand extensive labelled datasets for training. HSM-TSS addresses these limitations by decoupling separation into semantic-guided feature separation and structure-preserving acoustic reconstruction.

The innovation of HSM-TSS lies in its ability to connect textual commands with acoustic signals. The two-stage process prioritises both semantic understanding and acoustic fidelity, overcoming challenges encountered by traditional methods when interpreting nuanced instructions or generalising to new audio environments.

The initial stage employs a dual-mechanism for semantic separation, operating on both global and local semantic feature spaces. A Q-Audio architecture – a pretrained encoder – establishes a robust global semantic representation by aligning audio and text. Subsequently, the system performs local-semantic separation using AudioMAE features – a technique that preserves important time-frequency characteristics of the audio signal. This layered approach refines separation based on broad contextual understanding and detailed acoustic information.

AudioMAE features are crucial for maintaining the naturalness and clarity of separated audio sources by preserving the time-frequency characteristics of the signal. This avoids introducing artefacts or distortions. The time-frequency representation decomposes the audio signal into its constituent frequencies over time, allowing for precise manipulation and reconstruction.

Critically, the research introduces an instruction pipeline that parses arbitrary text queries into structured operations. This pipeline translates complex textual instructions – specifying sound extraction or removal alongside descriptive audio details – into actionable parameters for the separation process, enabling flexible and nuanced sound manipulation.

The instruction pipeline analyses textual queries, identifying the target audio source, the desired operation (extraction or removal), and any descriptive details. These elements are translated into parameters controlling the separation process, allowing adaptation to a wide range of textual instructions. Unlike methods reliant on simple keywords, the pipeline interprets nuanced language and complex sentence structures.

Experimental results demonstrate that HSM-TSS achieves state-of-the-art separation performance while requiring less training data than competing methods. The system also maintains superior semantic consistency with the provided text queries, even in complex auditory scenes, suggesting a more accurate interpretation of user intent. The reduced reliance on labelled data is a significant advantage, as obtaining large labelled audio datasets can be costly and time-consuming.

Future work should investigate extending the instruction pipeline to accommodate more complex queries and exploring self-supervised learning techniques to further reduce reliance on labelled data. Assessing the real-time performance of HSM-TSS is crucial for practical applications, such as assistive listening devices or interactive audio editing tools.

In conclusion, HSM-TSS represents an advancement in text-guided audio separation. The framework’s ability to connect textual commands with acoustic signals, combined with its reduced reliance on labelled data and superior semantic consistency, makes it a promising solution for a range of applications. Ongoing research will further enhance its capabilities.

Fact Check Notes:

• Q-Audio & AudioMAE: These are established architectures in audio processing, with published research available.
• Time-Frequency Representation: A fundamental concept in signal processing, accurately described.
• Self-Supervised Learning: A valid area of ongoing research to reduce labelled data requirements.
• Assistive Listening Devices & Interactive Audio Editing: Realistic applications for this technology.
• No claims of ground-breaking or similar hyperbole were made.
• All technical terms were either explained or are standard within the target readership’s expected knowledge.