  Recently, self-supervised learning (SSL) techniques have been introduced to
solve the monaural speech enhancement problem. Due to the lack of using clean
phase information, the enhancement performance is limited in most SSL methods.
Therefore, in this paper, we propose a phase-aware self-supervised learning
based monaural speech enhancement method. The latent representations of both
amplitude and phase are studied in two decoders of the foundation autoencoder
(FAE) with only a limited set of clean speech signals independently. Then, the
downstream autoencoder (DAE) learns a shared latent space between the clean
speech and mixture representations with a large number of unseen mixtures. A
complex-cycle-consistent (CCC) mechanism is proposed to minimize the
reconstruction loss between the amplitude and phase domains. Besides, it is
noticed that if the speech features are extracted as the multi-resolution
spectra, the desired information distributed in spectra of different scales can
be studied to further boost the performance. The NOISEX and DAPS corpora are
used to generate mixtures with different interferences to evaluate the efficacy
of the proposed method. It is highlighted that the clean speech and mixtures
fed in FAE and DAE are not paired. Both ablation and comparison experimental
results show that the proposed method clearly outperforms the state-of-the-art
approaches.
