Shared Representation Learning for Reference-Guided Targeted Sound Detection
Abstract
Human listeners exhibit the remarkable ability to segregate a desired sound from complex acoustic scenes through selective auditory attention, motivating the study of Targeted Sound Detection (TSD). The task requires detecting and localizing a target sound in a mixture when a reference audio of that sound is provided. Prior approaches, rely on generating a sound-discriminative conditional embedding vector for the reference and pairing it with a mixture encoder, jointly optimized with a multi-task learning approach. In this work, we propose a unified encoder architecture that processes both the reference and mixture audio within a shared representation space, promoting stronger alignment while reducing architectural complexity. This design choice not only simplifies the overall framework but also enhances generalization to unseen classes. Following the multi-task training paradigm, our method achieves substantial improvements over prior approaches, surpassing existing methods and establishing a new state-of-the-art benchmark for targeted sound detection, with a segment-level F1 score of 83.15% and an overall accuracy of 95.17% on the URBAN-SED dataset.
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 paper presents a unified encoder framework for reference-guided targeted sound detection, achieving state-of-the-art performance and demonstrating robustness in real-world applications. The methodology and results contribute meaningfully to the field of audio machine learning, particularly in enhancing sound event detection capabilities.
Methodology
The proposed methodology introduces a unified encoder architecture that processes both reference and mixture audio in a shared representation space. This approach reduces architectural complexity and enhances feature alignment, which is a significant improvement over previous dual-branch designs. The methodology is well-structured, leveraging ConvNeXt for representation extraction and employing diverse fusion strategies, which are systematically evaluated. The multi-task learning paradigm further strengthens the model's performance by combining clip-level classification with frame-level detection, showcasing a comprehensive understanding of the task requirements.
Results & evaluation
The experimental evaluation is robust, utilizing well-defined datasets (URBAN-SED and UrbanSound8K) and establishing new benchmarks for performance metrics, particularly segment-level F1 scores. The results demonstrate substantial improvements over prior methods, indicating the effectiveness of the proposed approach. The evaluation also includes cross-domain generalization tests, which add depth to the findings and confirm the model's resilience to distributional shifts.
Reproducibility
The paper provides sufficient implementation details, including architecture specifications, training configurations, and data augmentation strategies, which facilitate reproducibility. However, the absence of a public code repository or demo URL limits the ease of reproduction for external researchers.
Limitations
One identified limitation is the reliance on specific datasets, which may not fully represent the diversity of real-world acoustic environments. Additionally, while the model shows strong performance on the benchmark datasets, its effectiveness in more complex or noisy real-world scenarios remains to be thoroughly validated.
Broader impact
The research has significant implications for various applications, including surveillance, multimedia retrieval, and smart assistants, where targeted sound detection is crucial. The ability to generalize to unseen classes enhances the model's applicability in real-world scenarios, potentially leading to advancements in audio processing technologies. The paper presents a unified encoder framework for reference-guided targeted sound detection, achieving state-of-the-art performance and demonstrating robustness in real-world applications. The methodology and results contribute meaningfully to the field of audio machine learning, particularly in enhancing sound event detection capabilities.
Author of this paper? Send a correction.