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Found 15 papers, 4 papers with code
From Speech-to-Speech Translation to Automatic Dubbing
We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing.
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on
Speech Enhancement
on CHiME-3
Efficient Trainable Front-Ends for Neural Speech Enhancement
Many neural speech enhancement and source separation systems operate in the time-frequency domain.
PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss
Neural network applications generally benefit from larger-sized models, but for current speech enhancement models, larger scale networks often suffer from decreased robustness to the variety of real-world use cases beyond what is encountered in training data.
Enhancing Audio Augmentation Methods with Consistency Learning
For tasks such as classification, there is a good case for learning representations of the data that are invariant to such transformations, yet this is not explicitly enforced by classification losses such as the cross-entropy loss.
Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output.
Semi-Supervised Singing Voice Separation with Noisy Self-Training
Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled data to improve the model's performance.
Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement
The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size and complexity.
Robust Audio Anomaly Detection
The presented approach doesn't assume the presence of labeled anomalies in the training dataset and uses a novel deep neural network architecture to learn the temporal dynamics of the multivariate time series at multiple resolutions while being robust to contaminations in the training dataset.
Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet
Neural speech synthesis models can synthesize high quality speech but typically require a high computational complexity to do so.
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation
Neural vocoders have recently demonstrated high quality speech synthesis, but typically require a high computational complexity.
Improved singing voice separation with chromagram-based pitch-aware remixing
Singing voice separation aims to separate music into vocals and accompaniment components.
Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model
As deep speech enhancement algorithms have recently demonstrated capabilities greatly surpassing their traditional counterparts for suppressing noise, reverberation and echo, attention is turning to the problem of packet loss concealment (PLC).
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets
In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech.
Semi-supervised Time Domain Target Speaker Extraction with Attention
In this work, we propose Exformer, a time-domain architecture for target speaker extraction.
