Search Results for author:
Found 29 papers, 11 papers with code
LMs with a Voice: Spoken Language Modeling beyond Speech Tokens
We present SPECTRON, a novel approach to adapting pre-trained language models (LMs) to perform speech continuation.
Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation
Applying a diffusion model Vocoder that was pretrained to model single-speaker voices on the output of a deterministic separation model leads to state-of-the-art separation results.
Ranked #1 on
Speech Separation
on Libri20Mix
Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models
At the heart of the method lies a sampling process that combines the estimation of the denoising model with a low-pass version of the new speaker's sample.
Neural Decoding with Optimization of Node Activations
The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered.
SepIt: Approaching a Single Channel Speech Separation Bound
We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech.
Ranked #2 on
Speech Separation
on Libri10Mix
KNN-Diffusion: Image Generation via Large-Scale Retrieval
Recent text-to-image models have achieved impressive results.
SegDiff: Image Segmentation with Diffusion Probabilistic Models
Since the diffusion model is probabilistic, it is applied multiple times, and the results are merged into a final segmentation map.
A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody
The Midi is represented in a way that is invariant to the musical scale, and the melody is represented, for the purpose of conditioning the harmony, by the content of each bar, viewed as a chord.
Zero-Shot Translation using Diffusion Models
In this work, we show a novel method for neural machine translation (NMT), using a denoising diffusion probabilistic model (DDPM), adjusted for textual data, following recent advances in the field.
Denoising Diffusion Gamma Models
Generative diffusion processes are an emerging and effective tool for image and speech generation.
Non Gaussian Denoising Diffusion Models
Moreover, we show that using a mixture of Gaussian noise variables in the diffusion process improves the performance over a diffusion process that is based on a single distribution.
Recovering AES Keys with a Deep Cold Boot Attack
In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys.
Many-Speakers Single Channel Speech Separation with Optimal Permutation Training
Single channel speech separation has experienced great progress in the last few years.
Ranked #1 on
Speech Separation
on Libri15Mix
Noise Estimation for Generative Diffusion Models
Generative diffusion models have emerged as leading models in speech and image generation.
Autoregressive Belief Propagation for Decoding Block Codes
We revisit recent methods that employ graph neural networks for decoding error correcting codes and employ messages that are computed in an autoregressive manner.
Single channel voice separation for unknown number of speakers under reverberant and noisy settings
The proposed approach is composed of several separation heads optimized together with a speaker classification branch.
SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation
In addition, our approach effectively preserves the interaural cues, which improves the accuracy of sound localization.
Audio and Speech Processing Sound
Voice Separation with an Unknown Number of Multiple Speakers
We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously.
Ranked #1 on
Speech Separation
on WSJ0-4mix
Molecule Property Prediction and Classification with Graph Hypernetworks
In this work, we demonstrate that the replacement of the underlying networks with hypernetworks leads to a boost in performance, obtaining state of the art results in various benchmarks.
A Gated Hypernet Decoder for Polar Codes
Hypernetworks were recently shown to improve the performance of message passing algorithms for decoding error correcting codes.
Hyper-Graph-Network Decoders for Block Codes
Neural decoders were shown to outperform classical message passing techniques for short BCH codes.
Unsupervised Singing Voice Conversion
The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another.
Unsupervised Polyglot Text To Speech
We present a TTS neural network that is able to produce speech in multiple languages.
Fitting New Speakers Based on a Short Untranscribed Sample
Learning-based Text To Speech systems have the potential to generalize from one speaker to the next and thus require a relatively short sample of any new voice.
Near Maximum Likelihood Decoding with Deep Learning
The proposed decoder is based on the neural Belief Propagation algorithm and the Automorphism Group.
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild.
Deep Learning Methods for Improved Decoding of Linear Codes
Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.
RNN Decoding of Linear Block Codes
We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes.
Learning to Decode Linear Codes Using Deep Learning
A novel deep learning method for improving the belief propagation algorithm is proposed.
