Ambisonic Target Sound Extraction Using Direction and Semantic Cues
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Solution Overview
Problem
Conventional spatial audio techniques for ambisonic recordings struggle to effectively isolate target sounds from nearby interference in acoustically crowded scenarios, as they primarily rely on spatial information and fail to separate sounds originating from similar directions.
Innovation Solution
A neural network-based approach that combines spatial and semantic information by using a symmetric encoder-decoder U-net neural network, integrating directional parameters and semantic embeddings to generate an enhanced audio signal that isolates or amplifies target sounds while suppressing or removing interfering sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatially-driven techniques (beamforming, directional loudness modification) are used to isolate target sound, then sound isolation is improved, but effectiveness deteriorates when interfering sounds originate from directions spatially close to the target sound
Solution Approach 1:
The patent combines spatial information (directional parameters indicating direction of arrival) with semantic information (text description of the target sound) to form a hybrid conditioning vector. This merged approach allows the neural network to distinguish target sounds from nearby interference by using both where the sound comes from and what the sound is, resolving the limitation of spatial-only methods when sources are closely spaced
Solution Approach 2:
The patent transforms the text description of the target sound into a semantic embedding vector using a semantic encoder, converting semantic information into a numerical representation that can be concatenated with directional parameters. This parameter transformation enables the neural network to process both spatial and semantic cues in a unified framework, improving separation effectiveness
2Device complexity
If conventional spatial processing techniques are used, then device complexity is reduced, but adaptability to diverse acoustic scenes deteriorates
Solution Approach 1:
The patent employs a neural network-based extraction model that can process various types of acoustic scenes by conditioning on both directional parameters and semantic embeddings. This universal approach allows the same system to adapt to different acoustic environments and target sound types without requiring scene-specific processing, achieving high versatility while maintaining reasonable complexity through a single unified model
Solution Approach 2:
The patent introduces a semantic encoder as an intermediary component that processes text descriptions and generates semantic embedding vectors. This intermediary transforms diverse semantic information into a standardized numerical format that can be effectively integrated with spatial parameters, enabling the system to adapt to various acoustic scenes through semantic conditioning
Data Source
AI summary
A method includes receiving an ambisonics recording within a scene. The ambisonics recording includes a target sound and other sounds in the scene. The method includes receiving directional parameters indicating a direction of a source of the target sound in the scene. The method includes receiving a text description of the target sound within the scene. The method includes processing, using a semantic encoder, the text description of the target sound to generate a semantic embedding vector. The method includes concatenating the semantic embedding vector with the directional parameters to generate a conditioning vector. The method includes processing, using a neural network conditioned on the conditioning vector, the ambisonics recording to generate an enhanced audio signal that isolates the target sound in the scene from the other sounds in the scene.