85 citations · 243 across the 11 of their papers we have counts for
11 papers
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…
Efficient Spoken Language Recognition via Multilabel Classification
Oriol Nieto, Zeyu Jin, Franck Dernoncourt +1
Spoken language recognition (SLR) is the task of automatically identifying the language present in a speech signal. Existing SLR models are either too computationally expensive or…
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…
Language-Guided Audio-Visual Source Separation via Trimodal Consistency
Reuben Tan, Arijit Ray, Andrea Burns +5
We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as tra…
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…
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,…
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…
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…
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…
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…
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…