116 citations · 674 across the 48 of their papers we have counts for
10 papers · 1 filter
Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training
Rong Gong, Xavier Serra
Music Information Retrieval (MIR) technologies have been proven useful in assisting western classical singing training. Jingju (also known as Beijing or Peking opera) singing is di…
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…
Creating an A Cappella Singing Audio Dataset for Automatic Jingju Singing Evaluation Research
Rong Gong, Rafael Caro Repetto, Xavier Serra
The data-driven computational research on automatic jingju (also known as Beijing or Peking opera) singing evaluation lacks a suitable and comprehensive a cappella singing audio da…
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…
Metrical-accent Aware Vocal Onset Detection in Polyphonic Audio
Georgi Dzhambazov, Andre Holzapfel, Ajay Srinivasamurthy +1
The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings. Starting with a hypothesis that the knowledge of the current positi…
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…