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Cocktail party problem is the scenario where it is difficult to separate ordistinguish individual speaker from a mixed speech from several speakers. Therehave been several researches going on in this field but the size and complexityof the model is being traded off with the accuracy and robustness of speechseparation. "Monaural multi-speaker speech separation" presents aspeech-separation model based on the Transformer architecture and its efficientforms. The model has been trained with the LibriMix dataset containing diversespeakers' utterances. The model separates 2 distinct speaker sources from amixed audio input. The developed model approaches the reduction incomputational complexity of the speech separation model, with minimum tradeoffwith the performance of prevalent speech separation model and it has shownsignificant movement towards that goal. This project foresees, a rise incontribution towards the ongoing research in the field of speech separationwith computational efficiency at its core.

Monaural Multi-Speaker Speech Separation Using Efficient Transformer Model