HyBeam: Hybrid Microphone-Beamforming Array-Agnostic Speech Enhancement for Wearables
Abstract
Speech enhancement is a fundamental challenge in signal processing, particularly when robustness is required across diverse acoustic conditions and microphone setups. Deep learning methods have been successful for speech enhancement, but often assume fixed array geometries, limiting their use in mobile, embedded, and wearable devices. Existing array-agnostic approaches typically rely on either raw microphone signals or beamformer outputs, but both have drawbacks under changing geometries. We introduce HyBeam, a hybrid framework that uses raw microphone signals at low frequencies and beamformer signals at higher frequencies, exploiting their complementary strengths while remaining highly array-agnostic. Simulations across diverse rooms and wearable array configurations demonstrate that HyBeam consistently surpasses microphone-only and beamformer-only baselines in PESQ, STOI, and SI-SDR. A bandwise analysis shows that the hybrid approach leverages beamformer directivity at high frequencies and microphone cues at low frequencies, outperforming either method alone across all bands.
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 HyBeam, a hybrid microphone-beamforming framework for array-agnostic speech enhancement in wearables. The technical contribution is significant, as it addresses a critical challenge in speech processing by effectively leveraging the strengths of both microphone and beamformer inputs, leading to improved performance across diverse conditions.
Methodology
The methodology presented in the paper is robust, introducing a hybrid framework that effectively combines raw microphone signals and beamformer outputs to enhance speech in diverse acoustic environments. The authors provide a comprehensive analysis of existing methods, highlighting their limitations and justifying the need for a hybrid approach. The design choices made for the hybrid model, particularly the bandwise input strategy, are well-founded based on empirical observations from the baseline models. The use of simulations to test the framework across various room configurations and microphone setups adds depth to the methodology.
Results & evaluation
The experimental evaluation is thorough, utilizing a well-defined setup that includes both seen and unseen array geometries to assess the generalization capabilities of the proposed method. The metrics used (PESQ, STOI, SI-SDR) are appropriate for the task of speech enhancement and provide a comprehensive view of performance. The results demonstrate clear advantages of the hybrid approach over baseline methods, reinforcing the effectiveness of the proposed model.
Reproducibility
The paper provides sufficient detail regarding the experimental setup, including the simulation of room acoustics and the training methodology. However, the lack of a publicly accessible code repository limits the reproducibility of the results. Including a demo or project URL would enhance the ability for others to replicate the findings.
Limitations
One limitation noted in the paper is the performance drop in the high-frequency range (4-8 kHz), which the authors acknowledge and suggest as a direction for future work. Additionally, while the hybrid model shows improvements, it may still be sensitive to extreme perturbations in microphone positioning, which could be explored further.
Broader impact
The implications of this research are significant, particularly for applications in wearable technology, telecommunication, and assistive devices. By improving speech enhancement in challenging acoustic environments, the HyBeam framework could enhance communication for users in various settings, including those with hearing impairments or in noisy environments. The paper presents HyBeam, a hybrid microphone-beamforming framework for array-agnostic speech enhancement in wearables. The technical contribution is significant, as it addresses a critical challenge in speech processing by effectively leveraging the strengths of both microphone and beamformer inputs, leading to improved performance across diverse conditions.
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