103 citations · 312 across the 26 of their papers we have counts for
9 papers · 1 filter
Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
Or Tal, Felix Kreuk, Yossi Adi
Recent progress in text-to-music generation has enabled models to synthesize high-quality musical segments, full compositions, and even respond to fine-grained control signals, e.g…
Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation
Or Tal, Alon Ziv, Itai Gat +2
We present JASCO, a temporally controlled text-to-music generation model utilizing both symbolic and audio-based conditions. JASCO can generate high-quality music samples condition…
Masked Audio Generation using a Single Non-Autoregressive Transformer
Alon Ziv, Itai Gat, Gael Le Lan +6
We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens. Unlike prior work, MAGNeT is comprised of a single-st…
Simple and Controllable Music Generation
Jade Copet, Felix Kreuk, Itai Gat +5
We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representati…
Audio Language Modeling using Perceptually-Guided Discrete Representations
Felix Kreuk, Yaniv Taigman, Adam Polyak +4
In this work, we study the task of Audio Language Modeling, in which we aim at learning probabilistic models for audio that can be used for generation and completion. We use a stat…
AudioGen: Textually Guided Audio Generation
Felix Kreuk, Gabriel Synnaeve, Adam Polyak +6
We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates a…
Formant Estimation and Tracking using Probabilistic Heat-Maps
Yosi Shrem, Felix Kreuk, Joseph Keshet
Formants are the spectral maxima that result from acoustic resonances of the human vocal tract, and their accurate estimation is among the most fundamental speech processing proble…
Self-supervised Speaker Diarization
Yehoshua Dissen, Felix Kreuk, Joseph Keshet
Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization. Inarguably, a significant part of this success is due to t…
Hide and Speak: Towards Deep Neural Networks for Speech Steganography
Felix Kreuk, Yossi Adi, Bhiksha Raj +2
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier. Traditionally, digital signal processing techniques, suc…