Recently, masked autoencoders have been found to be beneficial in learning robust representations of audio for speech classification tasks. Following these studies, we leverage …

This repo hosts the code and models of "Masked Autoencoders that Listen". Resources

Nov 15, 2023 · A bubble chart has been provided in Fig. 6 to show which combination of use case and audio length has a greater number of equal values when comparing their waveforms. As …

Audio Super-resolution with Robust Speech Representation Learning of Masked Autoencoder, S.-B. Kim, S.-H. Lee, H.-Y. Choi, S.-W. Lee, IEEE Trans. on Audio, Speech and Language …

Our work also uses the masked autoencod-ing framework, but jointly models both audio and video, and is demonstrated on both unimodal (i.e. video-only and audio-only) and audiovisual …

Jan 1, 2024 · In this paper, we propose an upper-band masking strategy with the initialization of the mask token, which is simple but efficient for audio super-resolution. Furthermore, we …

Initially, we pre-train the masked autoencoder using a random masking strategy. Subsequently, the generator is jointly trained with the pre-trained encoder of masked autoencoder. For audio...

We introduce a diffusion-based generative model, AudioSR, that is capable of performing robust audio super-resolution on versatile audio types, including sound effects, music, and speech. …

In this paper, we propose Fre-Painter, a robust neural audio super-resolution system that utilizes robust speech represen-tation learning using MAE and several masking strategies. We utilize a...

Jul 13, 2022 · This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the …