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QUT-NOISE-TIMIT
The QUT-NOISE-TIMIT dataset is a dataset for speech enhancement. It consists of clean speech and noise. -
Database of Multichannel In-Ear and Behind-the-Ear Head-Related and Binaural ...
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. -
INTERSPEECH 2020 Deep Noise Suppression Challenge
The dataset used for evaluation of the proposed deep binaural MFMVDR filter, comprising measured binaural room impulse responses and clean speech and noise. -
Clarity-2021 Challenges: Machine Learning Challenges for Advancing Hearing Ai...
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. -
Noisy Speech Database for Training Speech Enhancement Algorithms and TTS Models
Noisy speech database for training speech enhancement algorithms and TTS models. -
SELF-SUPERVISED SPEECH QUALITY ESTIMATION AND ENHANCEMENT USING ONLY CLEAN SP...
The proposed self-supervised speech quality estimator trained only on clean speech. -
Database in [28]
The database in [28] which was used to evaluate SEGAN in [14]. -
VoiceBank DEMAND dataset
Speech enhancement dataset -
METRIC-ORIENTED SPEECH ENHANCEMENT USING DIFFUSION PROBABILISTIC MODEL
Diffusion probabilistic model for speech enhancement -
TIMIT dataset
The dataset used in this paper is a collection of phonetically and phonologically local allophonic distribution in English, where voiceless stops surface as aspirated... -
Speech enhancement generative adversarial network
Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those... -
Generative Pre-Training for Speech
Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate... -
Reverberation and Noise Contaminated Speech Datasets
Training and test datasets were generated by contaminating the clean data with reverberation and noise. -
Noisy mixtures dataset
The dataset used in the paper is a selection of 14 noisy mixtures created manually from the Voice Bank speech corpus. -
Dataset for speech enhancement
The dataset used in the paper is a selection of ten British English speakers – both male and female – from the Voice Bank speech corpus, each of which has around 400 clean... -
MultiCorpus
The MultiCorpus dataset is a diverse and acoustically adverse dataset generated by mixing reverberant speech and reverberant noise using speech utterances, noise segments and...
