The authors examine more recent and more sophisticated approaches using gated neural networks to separate mixed audio recordings that contain more than one speaker. The model proposed in this study trains separate models for different numbers of speakers and selects the one appropriate to the number present in the audio input. This is also especially impressive, as compared to the other modern systems, in the tasks involving overlapping speech and more than two talkers. The method overcomes unbalanced clusters due to dominating speakers and environmental noise problems, so it is practical. The survey covers an HMM-based audio separation discussion comparing modern machine learning methods with a qualitative focus on their application areas. Addressing such aspects as scalability, computation cost, and stability, in some sense, the methods offered ease of challenges in the fields of teleconferencing, speech enhancement, and audio reachability. Finally, as one of the contributions of this paper, the work discusses the limitations of the current trends and, in particular, the role of semi-supervised learning and generative models as well as multimodel fusion in transforming the future of audio processing.