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Found 19 papers, 7 papers with code
Audio Dialogues: Dialogues dataset for audio and music understanding
Existing datasets for audio understanding primarily focus on single-turn interactions (i. e. audio captioning, audio question answering) for describing audio in natural language, thus limiting understanding audio via interactive dialogue.
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities
Augmenting large language models (LLMs) to understand audio -- including non-speech sounds and non-verbal speech -- is critically important for diverse real-world applications of LLMs.
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram
In this work, we present CleanUNet 2, a speech denoising model that combines the advantages of waveform denoiser and spectrogram denoiser and achieves the best of both worlds.
Data Redaction from Conditional Generative Models
Deep generative models are known to produce undesirable samples such as harmful content.
Can Membership Inferencing be Refuted?
Given a machine learning model, a data point and some auxiliary information, the goal of an MI attack is to determine whether the data point was used to train the model.
Approximate Data Deletion in Generative Models
Using this framework, we introduce a fast method for approximate data deletion and a statistical test for estimating whether or not training points have been deleted.
Data Redaction from Pre-trained GANs
Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness.
Speech Denoising in the Waveform Domain with Self-Attention
In this work, we present CleanUNet, a causal speech denoising model on the raw waveform.
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
The RFNet refines the coarse output of the CGNet and further improves quality of the completed point cloud.
On Fast Sampling of Diffusion Probabilistic Models
In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models.
Understanding Instance-based Interpretability of Variational Auto-Encoders
Instance-based interpretation methods have been widely studied for supervised learning methods as they help explain how black box neural networks predict.
Universal Approximation of Residual Flows in Maximum Mean Discrepancy
Normalizing flows are a class of flexible deep generative models that offer easy likelihood computation.
A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space
Hyperbolic embeddings are a class of representation learning methods that offer competitive performances when data can be abstracted as a tree-like graph.
DiffWave: A Versatile Diffusion Model for Audio Synthesis
In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation.
Ranked #2 on
Speech Synthesis
on LJSpeech
The Expressive Power of a Class of Normalizing Flow Models
Normalizing flows have received a great deal of recent attention as they allow flexible generative modeling as well as easy likelihood computation.
Fastened CROWN: Tightened Neural Network Robustness Certificates
We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints.
Convergence Analysis of the Dynamics of a Special Kind of Two-Layered Neural Networks with $\ell_1$ and $\ell_2$ Regularization
In this paper, we made an extension to the convergence analysis of the dynamics of two-layered bias-free networks with one $ReLU$ output.
WristAuthen: A Dynamic Time Wrapping Approach for User Authentication by Hand-Interaction through Wrist-Worn Devices
This paper presents a novel user authentication system through wrist-worn devices by analyzing the interaction behavior with users, which is both accurate and efficient for future usage.
Generative Adversarial Networks with Inverse Transformation Unit
In this paper we introduce a new structure to Generative Adversarial Networks by adding an inverse transformation unit behind the generator.
