11 papers with code • 1 benchmarks • 1 datasets
Bandwidth extension is the task of expanding the bandwidth of a signal in a way that approximates the original or desired higher bandwidth signal.
Most implemented papers
On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks
In this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension.
HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models.
Super-Resolution with Deep Convolutional Sufficient Statistics
Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its low-resolution corrupted observation.
Wavenet based low rate speech coding
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used.
TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining
We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension.
Neural Vocoder is All You Need for Speech Super-resolution
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals.
EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones
In this paper, we present Extreme Bandwidth Extension Network (EBEN), a Generative Adversarial network (GAN) that enhances audio measured with body-conduction microphones.
Solving Audio Inverse Problems with a Diffusion Model
This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting.
Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration
In this paper, we systematically compare the performance of generative diffusion models and discriminative approaches on different speech restoration tasks.