This survey paper provides a comprehensive overview of the recent advancements and
challenges in applying large language models to the field of audio signal processing. Audio …

We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural
networks. It consists in a streaming encoder-decoder architecture with quantized latent …

We present SoundStream, a novel neural audio codec that can efficiently compress speech,
music and general audio at bitrates normally targeted by speech-tailored codecs …

Removing background noise from speech audio has been the subject of considerable effort,
especially in recent years due to the rise of virtual communication and amateur recordings …

This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an
extension of the open-source speech processing toolkit FunASR. FunCodec provides …

We propose TRAMBA, a hybrid transformer and Mamba architecture for acoustic and bone
conduction speech enhancement, suitable for mobile and wearable platforms. Bone …

The recent advancement of end-to-end neural audio codecs enables compressing audio at
very low bitrates while reconstructing the output audio with high fidelity. Nonetheless, such …

We present AERO, a audio super-resolution model that processes speech and music
signals in the spectral domain. AERO is based on an encoder-decoder architecture with …

This paper proposes Fre-Painter, a high-fidelity audio super-resolution system that utilizes
robust speech representation learning with various masking strategies. Recently, masked …

Generative adversarial networks have recently demonstrated outstanding performance in
neural vocoding outperforming best autoregressive and flow-based models. In this paper …