LuSeeL: Language-queried Binaural Universal Sound Event Extraction and Localization
Abstract
Most universal sound extraction algorithms focus on isolating a target sound event from single-channel audio mixtures. However, the real world is three-dimensional, and binaural audio, which mimics human hearing, can capture richer spatial information, including sound source location. This spatial context is crucial for understanding and modeling complex auditory scenes, as it inherently informs sound detection and extraction. In this work, we propose a language-driven universal sound extraction network that isolates text-described sound events from binaural mixtures by effectively leveraging the spatial cues present in binaural signals. Additionally, we jointly predict the direction of arrival (DoA) of the target sound using spatial features from the extraction network. This dual-task approach exploits complementary location information to improve extraction performance while enabling accurate DoA estimation. Experimental results on the in-the-wild AudioCaps dataset show that our proposed LuSeeL model significantly outperforms single-channel and uni-task baselines.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this work is the introduction of LuSeeL, a language-driven framework for joint binaural sound extraction and localization, which significantly improves performance by leveraging spatial information and multimodal inputs. This research addresses critical challenges in audio scene understanding and paves the way for advanced applications in various domains.
Methodology
The proposed methodology introduces a dual-task framework that integrates sound event extraction and localization using binaural audio, leveraging a hybrid transformer architecture. The use of a T5 text encoder for processing language queries is innovative, allowing for flexible and context-aware sound extraction. The architecture effectively combines temporal and spectral information, enhancing the model's ability to handle complex auditory scenes. The integration of spatial features through the GCC-PHAT encoder further strengthens the model's performance in localization tasks. Overall, the methodology is well-structured and addresses critical challenges in audio scene analysis.
Results & evaluation
The experimental evaluation is robust, utilizing the AudioCaps dataset, which is appropriate for the tasks at hand. The results demonstrate significant improvements over baseline models in both sound extraction and localization tasks, with clear quantitative metrics (SI-SNRi, SDRi, accuracy, MAE) reported. The experiments also include ablation studies that effectively illustrate the contributions of each component of the model, validating the proposed dual-task approach. However, the paper could benefit from additional qualitative analysis of the results to provide deeper insights into model performance across different scenarios.
Reproducibility
The paper provides a detailed description of the model architecture, training procedures, and loss functions, which aids in reproducibility. However, the absence of a publicly available code repository or demo URL limits the ability for other researchers to replicate the results independently. Including such resources would enhance the paper's impact and facilitate further research in this area.
Limitations
One limitation of the study is the reliance on the AudioCaps dataset, which, while large, may not encompass all possible real-world scenarios encountered in diverse auditory environments. Additionally, the model's performance in extremely noisy or highly overlapping sound conditions remains to be evaluated. The paper also does not address the computational complexity of the proposed model, which may limit its applicability in real-time applications.
Broader impact
The implications of this research are significant, particularly in fields such as augmented reality, robotics, and assistive technologies, where accurate sound localization and extraction can enhance user experience and system performance. The ability to process binaural audio with language queries opens new avenues for intuitive human-machine interaction, potentially leading to more immersive and responsive systems. The main contribution of this work is the introduction of LuSeeL, a language-driven framework for joint binaural sound extraction and localization, which significantly improves performance by leveraging spatial information and multimodal inputs. This research addresses critical challenges in audio scene understanding and paves the way for advanced applications in various domains.
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