activity
20202024
most citedMultichannel-based learning for audio object extraction

6 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD2024

Universal Spatial Audio Transcoder

Amaia Sagasti, Davide Scaini, Daniel Arteaga

This paper addresses the challenges associated with both the conversion between different spatial audio formats and the decoding of a spatial audio format to a specific loudspeaker…

cs.SD2023★ 2 cited

Mono-to-stereo through parametric stereo generation

Joan Serrà, Davide Scaini, Santiago Pascual +4

Generating a stereophonic presentation from a monophonic audio signal is a challenging open task, especially if the goal is to obtain a realistic spatial imaging with a specific pa…

cs.SD2021★ 1 cited

Upsampling layers for music source separation

Jordi Pons, Joan Serrà, Santiago Pascual +3

Upsampling artifacts are caused by problematic upsampling layers and due to spectral replicas that emerge while upsampling. Also, depending on the used upsampling layer, such artif…

cs.SD2021★ 6 cited

Multichannel-based learning for audio object extraction

Daniel Arteaga, Jordi Pons

The current paradigm for creating and deploying immersive audio content is based on audio objects, which are composed of an audio track and position metadata. While rendering an ob…

eess.AS2020★ 3 cited

Sound event localization and detection based on crnn using rectangular filters and channel rotation data augmentation

Francesca Ronchini, Daniel Arteaga, Andrés Pérez-López

Sound Event Localization and Detection refers to the problem of identifying the presence of independent or temporally-overlapped sound sources, correctly identifying to which sound…