The Conv-TasNet and Demucs algorithms, can differentiate between two mixed signals, such as music and speech, the mixing operation proceed without any support information. The network of convolutional time-domain audio separations is used in Conv-TasNet algorithm, while there is a new waveform-to-waveform model in Demucs algorithm. The Demucs algorithm utilizes a procedure like the audio generation model and sizable decoder capacity. The algorithms are not pretrained; so, the process of separation is blindly without any function of three Natural Languages (NL) detection. This research study evaluated the quality and execution time of the separation output signals. It focused on studying the effectiveness of NL in Both algorithms based on four sound signal experiments: (music & male), (music &female), (music & conversation), and finally (music & child). In addition, this research studies three NL, which are English, Arabic and Chinese. The results are evaluated based on R square and mir_eval libraries, mean absolute Error (MAE) scores and root mean square error (RMSE). Conv-TasNet has the highest Signal-to-distortion-Ratio (SDR) score is 9.21 of music at (music & female) experiment, and the highest SDR value of child signal is 8.14. The SDR score of music at (music & female) experiment is 7.8 during the Demucs algorithm, whereas child output signal has the highest SDR score 8.15. However, the average execution time of English experiment of Conv-TasNet is seven times faster than Demucs. For accuracy measure, RMSE indicates absolute values, and MAE handles the errors between observations and prediction signals. Both algorithms show high accuracy and excellent results in the separation process.
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