Posts by Collection

portfolio

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publications

Primary-Ambient Extraction in Audio Signals Using Adaptive Weighting and Principal Component Analysis

Published in The 13th Sound and Music Computing Conference (SMC), 2016

This paper is about separting the primary and ambient sources from a sounds mixture to be used in surround sound upmixing. We propose a PCA-based approach to apply the separation

Intelligibility of Sung Lyrics: A Pilot Study

Published in The 19th International Society for Music Information Retreival Conference ISMIR, 2017

This paper is about estimating the intelligibility of the singing voice in a given song. We propose a set of acoustic features that are relevant for estimating the intelligibility. We also propose an approach for labeling songs with an intelligibility score accroding to human perception

Primary-Ambient Source Separation for Upmixing to Surround Sound Systems

Published in The IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

This paper is about separting the primary and ambient sources from a sounds mixture to be used in surround sound upmixing. We propose a neural-network-based approach to apply the separation

Empirically Weighing the Importance of Decision Factors When Selecting Music to Sing

Published in The 19th International Society for Music Information Retreival Conference (ISMIR), 2018, 2018

This paper is about estimaing the singability of a given song and that factors that make one song more singable than another. We propose a number of acoustic features to automatically estimate the singability of a song

Audio-Based Auto-Tagging With Contextual Tags for Music

Published in The IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

This paper is about auto-tagging music tracks with context-related tags. The paper also presents a dataset of ∼50k tracks labelled with 15 different contexts.

Confidence-based Weighted Loss for Multi-label Classification with Missing Labels

Published in The 2020 International Conference on Multimedia Retrieval, 2020

This paper is about a weighted loss function that accounts for the missing labels in the training set that is easily usable in fine-tuning pre-trained models

Should we consider the users in contextual music auto-tagging models?

Published in 21st International Society for Music Information Retrieval Conference (ISMIR), 2020

This paper proposes a user-aware auto-tagging system for the contextual tags of music tracks

Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts

Published in 22nd International Society for Music Information Retrieval Conference (ISMIR), 2022

This paper proposes real-time predictions of the user listening context

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015