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Found 7 papers, 2 papers with code
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality
In this paper, we evaluate several MOS predictors based on wav2vec 2. 0 and the NISQA speech quality prediction model to explore the role of the training data, the influence of the system type, and the role of cross-domain features in SSL models.
MusicNet: Compact Convolutional Neural Network for Real-time Background Music Detection
With the recent growth of remote work, online meetings often encounter challenging audio contexts such as background noise, music, and echo.
Towards efficient models for real-time deep noise suppression
It is shown that the achievable speech quality is a function of network complexity, and show which models have better tradeoffs.
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
In this challenge, we open-sourced a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
In this challenge, we open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
A scalable noisy speech dataset and online subjective test framework
Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
Supervised Classifiers for Audio Impairments with Noisy Labels
The user feedback after the call can act as the ground truth labels for training a supervised classifier on a large audio dataset.
