8 datasets found

The QUT-NOISE-TIMIT dataset is a dataset for speech enhancement. It consists of clean speech and noise.

The dataset used for training and validation of the proposed deep binaural MFMVDR filter, comprising simulated binaural room impulse responses and clean speech and noise.

The dataset used for evaluation of the proposed deep binaural MFMVDR filter, comprising measured binaural room impulse responses and clean speech and noise.

The dataset used for training and validation of the proposed deep binaural MFMVDR filter, comprising simulated binaural room impulse responses and clean speech and noise.

The proposed self-supervised speech quality estimator trained only on clean speech.

The LRS2 dataset consists of 48,164 video clips from outdoor shows on BBC television. Each video is accompanied by an audio corresponding to a sentence with up to 100 characters.

The VoiceBank+DEMAND (VBDMD) 28-speaker dataset is a publicly available and popular benchmark dataset for single-channel speech enhancement.

Using power level difference for near field dual-microphone speech enhancement