activity
20172022
most citedA Deep Multimodal Approach for Cold-start Music Recommendation

80 citations · 150 across the 4 of their papers we have counts for

collaborators

6 papers

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.IR20206 cited

Multimodal Metric Learning for Tag-based Music Retrieval

Minz Won, Sergio Oramas, Oriol Nieto +2

Tag-based music retrieval is crucial to browse large-scale music libraries efficiently. Hence, automatic music tagging has been actively explored, mostly as a classification task,…

cs.SD2020

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…

stat.ML2018

Predicting Audio Advertisement Quality

Samaneh Ebrahimi, Hossein Vahabi, Matthew Prockup +1

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique…

cs.IR201780 cited

A Deep Multimodal Approach for Cold-start Music Recommendation

Sergio Oramas, Oriol Nieto, Mohamed Sordo +1

An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists…

cs.IR201764 cited

Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features

Sergio Oramas, Oriol Nieto, Francesco Barbieri +1

Music genres allow to categorize musical items that share common characteristics. Although these categories are not mutually exclusive, most related research is traditionally focus…