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Found 6 papers, 2 papers with code
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency.
Ranked #4 on
Audio Generation
on AudioCaps
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples.
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Combining this novel perspective of two-stage synthesis with advanced generative models (i. e., the diffusion models), the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples.
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training
In this paper, we propose InferGrad, a diffusion model for vocoder that incorporates inference process into training, to reduce the inference iterations while maintaining high generation quality.
A Granular Sieving Algorithm for Deterministic Global Optimization
With straightforward mathematical formulation applicable to both univariate and multivariate objective functions, the global minimum value and all the global minimizers are located through two decreasing sequences of compact sets in, respectively, the domain and range spaces.
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
