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Emilia Gómez

9 accepted papers

2022

Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (Extended Abstract)*

Songül Tolan, Annarosa Pesole, Fernando Martínez-Plumed, Enrique Fernández-Macías, José Hernández-Orallo, Emilia Gómez
IJCAI 2022poster

We present a framework for analysing the impact of AI on occupations. This framework maps 59 generic tasks from different occupational datasets to 14 cognitive abilities and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity in AI. The use of cognitive abilities…

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2021

Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Furkan Yesiler, Emilio Molina, Joan Serrà, Emilia Gómez
ICASSP 2021accepted

The setlist identification (SLI) task addresses a music recognition use case where the goal is to retrieve the metadata and times-tamps for all the tracks played in live music events. Due to various musical and non-musical changes in live performances, developing automatic SLI systems is still a cha…

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2021

Language-Sensitive Music Emotion Recognition Models: are We Really There Yet?

Juan Sebastián Gómez Cañón, Estefanía Cano, Ana Gabriela Pandrea, Perfecto Herrera, Emilia Gómez
ICASSP 2021accepted

Our previous research showed promising results when transferring features learned from speech to train emotion recognition models for music. In this context, we implemented a denoising autoencoder as a pretraining approach to extract features from speech in two languages (English and Mandarin). From…

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2021

Loopnet: Musical Loop Synthesis Conditioned on Intuitive Musical Parameters

Pritish Chandna, António Ramires, Xavier Serra, Emilia Gómez
ICASSP 2021accepted

Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create compositions. Taking such criteria into account,…

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2020

Accurate and Scalable Version Identification Using Musically-Motivated Embeddings

Furkan Yesiler, Joan Serrà, Emilia Gómez
ICASSP 2020accepted

The version identification (VI) task deals with the automatic detection of recordings that correspond to the same underlying musical piece. Despite many efforts, VI is still an open problem, with much room for improvement, specially with regard to combining accuracy and scalability. In this paper, w…

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2020

Content Based Singing Voice Extraction from a Musical Mixture

Pritish Chandna, Merlijn Blaauw, Jordi Bonada, Emilia Gómez
ICASSP 2020accepted

We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content. Our model follows an encoder-decoder architecture and takes as input the magnitude component of the spectrogram of a musical mixture with vocals. Th…

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2020

Neural Percussive Synthesis Parameterised by High-Level Timbral Features

António Ramires, Pritish Chandna, Xavier Favory, Emilia Gómez, Xavier Serra
ICASSP 2020accepted

We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. W…

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2019

End-to-end Sound Source Separation Conditioned on Instrument Labels

Olga Slizovskaia, Leo Kim, Gloria Haro, Emilia Gómez
ICASSP 2019accepted

Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-U-Net [1] model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicati…

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