A two-step approach for speech enhancement in low-SNR scenarios using cyclostationary beamforming and DNNs
Abstract
Deep Neural Networks (DNNs) often struggle to suppress noise at low signal-to-noise ratios (SNRs). This paper addresses speech enhancement in scenarios dominated by harmonic noise and proposes a framework that integrates cyclostationarity-aware preprocessing with lightweight DNN-based denoising. A cyclic minimum power distortionless response (cMPDR) spectral beamformer is used as a preprocessing block. It exploits the spectral correlations of cyclostationary noise to suppress harmonic components prior to learning-based enhancement and does not require modifications to the DNN architecture. The proposed pipeline is evaluated in a single-channel setting using two DNN architectures: a simple and lightweight convolutional recurrent neural network (CRNN), and a state-of-the-art model, namely ultra-low complexity network (ULCNet). Experiments on synthetic data and real-world recordings dominated by rotating machinery noise demonstrate consistent improvements over end-to-end DNN baselines, particularly at low SNRs. Remarkably, a parameter-efficient CRNN with cMPDR preprocessing surpasses the performance of the larger ULCNet operating on raw or Wiener-filtered inputs. These results indicate that explicitly incorporating cyclostationarity as a signal prior is more effective than increasing model capacity alone for suppressing harmonic interference.
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
This paper presents a novel hybrid framework for speech enhancement that effectively combines cyclostationarity-aware preprocessing with DNN-based denoising, showcasing significant performance improvements in low-SNR scenarios. The methodology is well-supported by rigorous experimentation, and the findings could have substantial implications for real-world applications in noisy environments.
Methodology
The proposed methodology effectively integrates cyclostationarity-aware preprocessing with DNN-based denoising, utilizing a cyclic minimum power distortionless response (cMPDR) beamformer to enhance speech in low-SNR environments. This two-step approach is innovative as it leverages the unique properties of cyclostationary noise without necessitating modifications to the DNN architecture, thus maintaining a lightweight model. The choice of using both a simple convolutional recurrent neural network (CRNN) and a more complex ultra-low complexity network (ULCNet) for evaluation provides a robust comparison of the method's effectiveness across different model complexities.
Results & evaluation
The experimental evaluation is thorough, employing both synthetic and real-world datasets to demonstrate the method's effectiveness. The results consistently show significant improvements in performance metrics such as SI-SDR and DNSMOS, particularly in low-SNR conditions. The paper clearly delineates the performance gains achieved through the proposed preprocessing step, establishing a strong case for the benefits of incorporating cyclostationarity in speech enhancement tasks.
Reproducibility
The paper provides sufficient implementation details, including architecture specifications, training protocols, and hyperparameters, which facilitate reproducibility. The availability of the code on GitHub further enhances the potential for other researchers to replicate the study and build upon the findings.
Limitations
One limitation noted is the reliance on stable noise frequencies for the cMPDR to be effective, which may not hold in all real-world scenarios. Additionally, the method's performance on non-cyclostationary noise types could be less effective, as indicated by the results on the DNS dataset.
Broader impact
The proposed approach has significant implications for applications in industrial environments where effective speech communication is crucial amidst high levels of noise. By improving speech enhancement technologies, this work could enhance the usability of hearing aids and communication devices in challenging acoustic conditions, potentially benefiting a wide range of users. This paper presents a novel hybrid framework for speech enhancement that effectively combines cyclostationarity-aware preprocessing with DNN-based denoising, showcasing significant performance improvements in low-SNR scenarios. The methodology is well-supported by rigorous experimentation, and the findings could have substantial implications for real-world applications in noisy environments.
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