571 citations · 929 across the 39 of their papers we have counts for
45 papers
GS-Codec: A Gaussian-Splatting Bottleneck for Neural Audio Coding
Ron Aluf, Alon Canfi, Eliya Nachmani
Neural audio codecs compress waveforms into compact discrete tokens that underpin speech language models, real-time communication, and large-scale audio storage. Almost every domin…
Multi Codec Discrete Diffusion Model for Text Guided Speech Inpainting and Editing
Iftach Shoham, Tali Dror, Oren Gal +3
Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utterance. Speech inpainting rest…
SAFEdit: Does Multi-Agent Decomposition Resolve the Reliability Challenges of Instructed Code Editing?
Noam Tarshish, Nofar Selouk, Daniel Hodisan +4
Instructed code editing is a significant challenge for large language models (LLMs). On the EditBench benchmark, 39 of 40 evaluated models obtain a task success rate (TSR) below 60…
ILRR: Inference-Time Steering Method for Masked Diffusion Language Models
Eden Avrahami, Eliya Nachmani
Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for inference-time control remain relativel…
SAQ: Stabilizer-Aware Quantum Error Correction Decoder
David Zenati, Eliya Nachmani
Quantum Error Correction (QEC) decoding faces a fundamental accuracy-efficiency tradeoff. Classical methods like Minimum Weight Perfect Matching (MWPM) exhibit variable performance…
Two-Dimensional Quantization for Geometry-Aware Audio Coding
Tal Shuster, Eliya Nachmani
Recent neural audio codecs have achieved impressive reconstruction quality, typically relying on quantization methods such as Residual Vector Quantization (RVQ), Vector Quantizatio…
FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video
Rotem Ezra, Hedi Zisling, Nimrod Berman +5
Diffusion models have become state-of-the-art generative models for images, audio, and video, yet enabling fine-grained controllable generation, i.e., continuously steering specifi…
Token-Based Audio Inpainting via Discrete Diffusion
Tali Dror, Iftach Shoham, Moshe Buchris +4
Audio inpainting seeks to restore missing segments in degraded recordings. Previous diffusion-based methods exhibit impaired performance when the missing region is large. We introd…
Differential Mamba
Nadav Schneider, Itamar Zimerman, Eliya Nachmani
Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by prom…
Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models
Omer Luxembourg, Haim Permuter, Eliya Nachmani
Masked diffusion language models (MDLMs) promise fast, non-autoregressive text generation, yet existing samplers, which pick tokens to unmask based on model confidence, ignore inte…
Toward Optimal ANC: Establishing Mutual Information Lower Bound
François Derrida, Shahar Lutati, Eliya Nachmani
Active Noise Cancellation (ANC) algorithms aim to suppress unwanted acoustic disturbances by generating anti-noise signals that destructively interfere with the original noise in r…
Hybrid Mamba-Transformer Decoder for Error-Correcting Codes
Shy-el Cohen, Yoni Choukroun, Eliya Nachmani
We introduce a novel deep learning method for decoding error correction codes based on the Mamba architecture, enhanced with Transformer layers. Our approach proposes a hybrid deco…
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
Ofir Yaish, Yehuda Mishaly, Eliya Nachmani
We introduce a new paradigm for active sound modification: Active Speech Enhancement (ASE). While Active Noise Cancellation (ANC) algorithms focus on suppressing external interfere…
Deep Active Speech Cancellation with Mamba-Masking Network
Yehuda Mishaly, Lior Wolf, Eliya Nachmani
We present a novel deep learning network for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively canceling both noise and spee…
Zero-Shot Mono-to-Binaural Speech Synthesis
Alon Levkovitch, Julian Salazar, Soroosh Mariooryad +4
We present ZeroBAS, a neural method to synthesize binaural audio from monaural audio recordings and positional information without training on any binaural data. To our knowledge,…
SimulTron: On-Device Simultaneous Speech to Speech Translation
Alex Agranovich, Eliya Nachmani, Oleg Rybakov +5
Simultaneous speech-to-speech translation (S2ST) holds the promise of breaking down communication barriers and enabling fluid conversations across languages. However, achieving acc…
Decision S4: Efficient Sequence-Based RL via State Spaces Layers
Shmuel Bar-David, Itamar Zimerman, Eliya Nachmani +1
Recently, sequence learning methods have been applied to the problem of off-policy Reinforcement Learning, including the seminal work on Decision Transformers, which employs transf…
Translatotron 3: Speech to Speech Translation with Monolingual Data
Eliya Nachmani, Alon Levkovitch, Yifan Ding +3
This paper presents Translatotron 3, a novel approach to unsupervised direct speech-to-speech translation from monolingual speech-text datasets by combining masked autoencoder, uns…
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
Eliya Nachmani, Alon Levkovitch, Roy Hirsch +6
We present Spectron, a novel approach to adapting pre-trained large language models (LLMs) to perform spoken question answering (QA) and speech continuation. By endowing the LLM wi…
Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation
Shahar Lutati, Eliya Nachmani, Lior Wolf
The problem of speech separation, also known as the cocktail party problem, refers to the task of isolating a single speech signal from a mixture of speech signals. Previous work o…
Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models
Alon Levkovitch, Eliya Nachmani, Lior Wolf
We present a novel way of conditioning a pretrained denoising diffusion speech model to produce speech in the voice of a novel person unseen during training. The method requires a…
Neural Decoding with Optimization of Node Activations
Eliya Nachmani, Yair Be'ery
The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss te…
SepIt: Approaching a Single Channel Speech Separation Bound
Shahar Lutati, Eliya Nachmani, Lior Wolf
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. Using the bound, we are a…
KNN-Diffusion: Image Generation via Large-Scale Retrieval
Shelly Sheynin, Oron Ashual, Adam Polyak +4
Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains w…