  This paper presents a review of multi-objective deep learning methods that
have been introduced in the literature for speech denoising. After stating an
overview of conventional, single objective deep learning, and hybrid or
combined conventional and deep learning methods, a review of the mathematical
framework of the multi-objective deep learning methods for speech denoising is
provided. A representative method from each speech denoising category, whose
codes are publicly available, is selected and a comparison is carried out by
considering the same public domain dataset and four widely used objective
metrics. The comparison results indicate the effectiveness of the
multi-objective method compared with the other methods, in particular when the
signal-to-noise ratio is low. Possible future improvements that can be achieved
are also mentioned.
