Sentence examples for for stereo speech from inspiring English sources

Time-frequency (T-F) masking is an effective method for stereo speech source separation.

More specifically, for stereo speech separation task, the target location is a natural choice of the output of the network.

Many features can be used for stereo speech separation, such as IPD or ITD [33], ILD or interaural intensity differences (IID) [33], and the MV cue.

The success of deep neural networks (DNNs) in these applications inspires us to investigate its potential for improving the performance of stereo speech source separation algorithms.

In this paper, we focus on the multiuser stereo speech source separation in reverberation environments and present a new approach for T-F assignment and mask estimation based on DNNs [23].

In this paper, we propose a new stereo speech separation system where deep neural networks are used to generate soft T-F mask for separation.

In this work, we used stereo speech recordings to build stereo GMM to model the joint probability of clean and noisy speech.

We have presented a new localization-based stereo speech separation system using deep networks.

In [31], Jiang et al. first introduced DNNs to stereo speech separation.

Compared with the monaural segregation of reverberant speech in [29], the stereo speech separation in [31] tends to be more robust due to the use of spatial information.

Present your theme through multiple approaches for stereo impact.