activity
20172023
most citedEnd-to-end learning for music audio tagging at scale

85 citations · 243 across the 11 of their papers we have counts for

collaborators
Showing cs.SDShow all

5 papers · 1 filter

cs.SD2023

Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries

Julia Wilkins, Justin Salamon, Magdalena Fuentes +2

Finding the right sound effects (SFX) to match moments in a video is a difficult and time-consuming task, and relies heavily on the quality and completeness of text metadata. Retri…

cs.SD2023

Audio-Text Models Do Not Yet Leverage Natural Language

Ho-Hsiang Wu, Oriol Nieto, Juan Pablo Bello +1

Multi-modal contrastive learning techniques in the audio-text domain have quickly become a highly active area of research. Most works are evaluated with standard audio retrieval an…

cs.SD2022

Music Enhancement via Image Translation and Vocoding

Nikhil Kandpal, Oriol Nieto, Zeyu Jin

Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ. This pape…

cs.SD2020★ 4 cited

Mood Classification Using Listening Data

Filip Korzeniowski, Oriol Nieto, Matthew McCallum +3

The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music. These collections tend to require automatic methods for predicting…

cs.SD2017★ 85 cited

End-to-end learning for music audio tagging at scale

Jordi Pons, Oriol Nieto, Matthew Prockup +3

The lack of data tends to limit the outcomes of deep learning research, particularly when dealing with end-to-end learning stacks processing raw data such as waveforms. In this stu…