COMPARATIVE ANALYSIS OF DISCRIMINATIVE DEEP LEARNING-BASED NOISE REDUCTION METHODS IN LOW SNR SCENARIOS

Abstract

In this study, we conduct a comparative analysis of deep learning-based noise reduction methods in low signal-to-noise ratio scenarios. Our investigation primarily focuses on five key aspects: The impact of training data, the influence of various loss functions, the effectiveness of noise and speech estimation techniques, the efficacy of masking, mapping, and deep filtering methodologies, and the exploration of different model capacities on noise reduction performance and speech quality. Through comprehensive experimentation, we provide insights into the strengths, weaknesses, and applicability of these methods in low SNR environments. The findings derived from our analysis are intended to assist both researchers and practitioners in selecting better techniques tailored to their specific applications within the domain of low SNR noise reduction.

Evaluation Scenarios

In our work, we evaluate different SOTA discriminative deep learning-based noise reduction methods in various SNR scenraios.

Following you can find some processed samples with different Methods:

Item 1 (SNR: -4dB, Speaker: Female)

Item 2 (SNR: -6dB, Speaker: Female)

Item 3 (SNR: -2dB, Speaker: Male)

Item 4 (SNR: -2dB, Speaker: Male)

Item 5 (SNR: 0dB, Speaker: Male)

Item 6 (SNR: -14dB, Speaker: Female)

Item 7 (SNR: -6dB, Speaker: Female)

Item 8 (SNR: -11dB, Speaker: Female)

Item 9 (SNR: -4dB, Speaker: Female)

Item 10 (SNR: -13dB, Speaker: Female)

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