Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised
learning (SSL) targets discovering general representations from large-scale data. This …

We introduce MusicLM, a model generating high-fidelity music from text descriptions such
as" a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of …

Self-supervised learning (SSL) has proven vital for advancing research in natural language
processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on …

We introduce Noise2Music, where a series of diffusion models is trained to generate high-
quality 30-second music clips from text prompts. Two types of diffusion models, a generator …

In the domain of audio processing, Transfer Learning has facilitated the rise of Self-
Supervised Learning and Zero-Shot Learning techniques. These approaches have led to …

We summarize the results of a host of efforts using giant automatic speech recognition (ASR)
models pre-trained using large, diverse unlabeled datasets containing approximately a …

We introduce COLA, a self-supervised pre-training approach for learning a general-purpose
representation of audio. Our approach is based on contrastive learning: it learns a …

Audio tagging is an active research area and has a wide range of applications. Since the
release of AudioSet, great progress has been made in advancing model performance, which …

Inspired by the recent progress in self-supervised learning for computer vision that
generates supervision using data augmentations, we explore a new general-purpose audio …