Frequency-domain monaural speech enhancement has been extensively studied for over 60 years, and a great number of methods have been proposed and applied to many devices. In the last decade, monaural speech enhancement has made tremendous progress with the advent and development of deep learning, and performance using such methods has been greatly improved relative to traditional methods. This survey paper first provides a comprehensive overview of traditional and deep-learning methods for monaural speech enhancement in the frequency domain. The fundamental assumptions of each approach are then summarized and analyzed to clarify their limitations and advantages. A comprehensive evaluation of some typical methods was conducted using the WSJ + Deep Noise Suppression (DNS) challenge and Voice Bank + DEMAND datasets to give an intuitive and unified comparison. The benefits of monaural speech enhancement methods using objective metrics relevant for normal-hearing and hearing-impaired listeners were evaluated. The objective test results showed that compression of the input features was important for simulated normal-hearing listeners but not for simulated hearing-impaired listeners. Potential future research and development topics in monaural speech enhancement are suggested.

Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods.

MAMGAN: Multiscale attention metric GAN for monaural speech enhancement in the time domain

A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem

Deep-learning based full-band speech enhancement methods have gained increasing proliferation in recent years. To balance between denoising performance and computational complexity, mainstream full-band approaches typically utilize the compressed perceptual-motivated features with relatively low frequency resolution in middle and high frequencies to recover the full-band spectrum, limiting the upper bound of speech quality. Recently, sub-band fusion based approaches have been developed, where the low-frequency and high-frequency bands are tackled separately, thus neglecting the full-band spectral pattern and cross-band dependency. This paper proposes a dual-stage full- and sub-band integration network, dubbed FSI-Net, to simultaneously leverage the coarse-grained full-band spectral pattern and the fine-grained sub-band spectral details for the full-band speech enhancement task. Concretely, in the first stage, only coarse denoising is performed using the compressed ERB-scaled spectrum to capture the global full-band spectral context, so as to decrease the computational overhead. In the second stage, because the sub-band spectral characteristics of speech vary among different frequency bands, we elaborately devise two sub-networks to refine the low-frequency and high-frequency bands separately in the complex domain. To fully capitalize on cross-band guidance, we employ a band-guided encoder to provide external knowledge for the high-frequency bands. Extensive experiments show that the proposed method consistently outperforms state-of-the-art one-stage full-band and sub-band fusion based baselines in terms of various evaluation metrics. 

FSI-Net: A dual-stage full- and sub-band integration network for full-band speech enhancement

The highly imbalanced power spectral density of full-band speech signals poses a significant challenge to full-band speech enhancement, and the commonly used spectral features that mimic the behavior of the human auditory system are not an optimal choice for full-band speech enhancement. In this paper, a learnable spectral dimension compression mapping is proposed to effectively compress the spectral feature along frequency, preserving high resolution in low frequencies while compressing information in high frequencies in a more flexible manner. Experimental results verify that the proposed method can be easily combined with different full-band speech enhancement models and achieve better performance.

Learnable spectral dimension compression mapping for full-band speech enhancement.

Analysis of trade-offs between magnitude and phase estimation in loss functions for speech denoising and dereverberation

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Analysis of trade-offs between magnitude and phase estimation in loss functions for speech denoising and dereverberation