000			01715nam a22001817a 4500
003			NUST
005			20240927092642.0
082			_a621.382,AZI
100			_aAziz, Eemaan _9126138
245			_aOverlapped Speech Separation System (OSSS) / _cNC Eemaan Aziz, NC Hashir Rizwan, NC Ayesha Riaz, NC Usman Awan.
260			_aMCS, NUST _b Rawalpindi _c 2024
300			_a84 p
505			_aOverlapped Speech refers to speakers speaking simultaneously (i.e. speech mixture). Speech separation has long been an active research topic in the signal processing community, with its importance in a wide range of applications such as hearable devices and telecommunication systems. It is a fundamental problem for all higher-level speech processing tasks. With recent progress in deep neural networks, the separation performance has been significantly advanced by various new problems. The problem formulation of time-domain, end-to-end speech separation naturally arises to tackle the disadvantages in frequency-domain systems. We’ve used a dual path recurrent neural network for separation of mixed audios. DPRNN (Dual-Path RNN) primarily separates in the time domain for audio source separation. It leverages recurrent neural networks (RNNs) to process temporal sequences of audio data. DPRNN focuses on exploiting temporal dependencies within audio signals for effective separation. We looked into the training objectives for separating and improving the robustness under reverberant environments. This project is further analyzed and can be used as the basis for future works.
650			_aUG EE Project _9118090
651			_aBEE-57 _9125983
700			_aSupervisor Dr. Shibli Nisar _9112570
942			_2ddc _cPR
999			_c611907 _d611907