Aurchestra: Fine-Grained, Real-Time Soundscape Control on Resource-Constrained Hearables
Abstract
Hearables are becoming ubiquitous, yet their sound controls remain blunt: users can either enable global noise suppression or focus on a single target sound. Real-world acoustic scenes, however, contain many simultaneous sources that users may want to adjust independently. We introduce Aurchestra, the first system to provide fine-grained, real-time soundscape control on resource-constrained hearables. Our system has two key components: (1) a dynamic interface that surfaces only active sound classes and (2) a real-time, on-device multi-output extraction network that generates separate streams for each selected class, achieving robust performance for upto 5 overlapping target sounds, and letting users mix their environment by customizing per-class volumes, much like an audio engineer mixes tracks. We optimize the model architecture for multiple compute-limited platforms and demonstrate real-time performance on 6 ms streaming audio chunks. Across real-world environments in previously unseen indoor and outdoor scenarios, our system enables expressive per-class sound control and achieves substantial improvements in target-class enhancement and interference suppression. Our results show that the world need not be heard as a single, undifferentiated stream: with Aurchestra, the soundscape becomes truly programmable.
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
Aurchestra introduces a groundbreaking approach to soundscape control on hearables, enabling users to manipulate multiple sound sources independently in real-time. The combination of innovative methodology and practical applications positions this work as a significant contribution to the field of audio machine learning, although further enhancements in reproducibility and comparative analysis are necessary for broader acceptance.
Methodology
The methodology presented in Aurchestra is innovative as it combines a dynamic interface with a real-time multi-output extraction network tailored for resource-constrained hearables. The authors detail a robust architecture that allows for the detection and manipulation of multiple overlapping sound classes, which is a significant advancement over traditional binary noise cancellation systems. The use of on-device processing is particularly noteworthy, as it addresses the limitations of latency and resource usage in mobile devices. However, the paper could benefit from a more detailed description of the model architecture and the specific algorithms used for sound class detection and extraction.
Results & evaluation
The experimental evaluation is thorough, demonstrating the system's performance in real-world environments with diverse acoustic scenarios. The authors report substantial improvements in target-class enhancement and interference suppression, which are critical metrics for the effectiveness of soundscape control. However, the paper lacks a comprehensive comparison with existing methods, which would help contextualize the results and validate the claimed improvements. Additionally, further details on the datasets used for training and testing would enhance the credibility of the experimental findings.
Reproducibility
The paper does not provide sufficient details regarding the implementation of the system, such as the specific datasets, training procedures, or hyperparameters used in the model. This lack of transparency could hinder reproducibility, which is a crucial aspect of machine learning research. Including a supplementary material section or a dedicated repository with code and data would significantly improve this aspect.
Limitations
While the system shows promising results, there are limitations that need to be addressed. The performance in highly dynamic or noisy environments is not fully explored, and the scalability of the approach to more than five overlapping sounds is unclear. Additionally, the user interface design and user experience aspects are briefly mentioned but not thoroughly evaluated, which could impact the system's practical usability.
Broader impact
Aurchestra has the potential to revolutionize personal audio experiences, making it applicable in various fields such as augmented reality, hearing aids, and smart environments. By allowing users to customize their auditory experiences in real-time, it could enhance accessibility for individuals with hearing impairments and improve the overall quality of life in noisy urban settings. The implications for privacy and user control over their auditory environment are also significant, as this technology could empower users to manage their soundscapes actively. Aurchestra introduces a groundbreaking approach to soundscape control on hearables, enabling users to manipulate multiple sound sources independently in real-time. The combination of innovative methodology and practical applications positions this work as a significant contribution to the field of audio machine learning, although further enhancements in reproducibility and comparative analysis are necessary for broader acceptance.
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