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Found 21 papers, 6 papers with code
A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement
Despite noise suppression being a mature area in signal processing, it remains highly dependent on fine tuning of estimator algorithms and parameters.
Sound Audio and Speech Processing
LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
We demonstrate that LPCNet can achieve significantly higher quality than WaveRNN for the same network size and that high quality LPCNet speech synthesis is achievable with a complexity under 3 GFLOPS.
A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet
We demonstrate that LPCNet operating at 1. 6 kb/s achieves significantly higher quality than MELP and that uncompressed LPCNet can exceed the quality of a waveform codec operating at low bitrate.
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.
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).
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 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.
Multi-channel Opus compression for far-field automatic speech recognition with a fixed bitrate budget
Automatic speech recognition (ASR) in the cloud allows the use of larger models and more powerful multi-channel signal processing front-ends compared to on-device processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved singing voice separation with chromagram-based pitch-aware remixing
Singing voice separation aims to separate music into vocals and accompaniment components.
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.
Low-Bitrate Redundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder
Robustness to packet loss is one of the main ongoing challenges in real-time speech communication.
Framewise WaveGAN: High Speed Adversarial Vocoder in Time Domain with Very Low Computational Complexity
GAN vocoders are currently one of the state-of-the-art methods for building high-quality neural waveform generative models.
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement.
LACE: A light-weight, causal model for enhancing coded speech through adaptive convolutions
Classical speech coding uses low-complexity postfilters with zero lookahead to enhance the quality of coded speech, but their effectiveness is limited by their simplicity.
NoLACE: Improving Low-Complexity Speech Codec Enhancement Through Adaptive Temporal Shaping
Speech codec enhancement methods are designed to remove distortions added by speech codecs.
Noise-Robust DSP-Assisted Neural Pitch Estimation with Very Low Complexity
Pitch estimation is an essential step of many speech processing algorithms, including speech coding, synthesis, and enhancement.
Real-time Stereo Speech Enhancement with Spatial-Cue Preservation based on Dual-Path Structure
The algorithm runs in real-time on 10-ms frames with a 40 ms of look-ahead.
