Details
Name
Serkan SulunCluster
Networked Intelligent SystemsRole
Research AssistantSince
11th March 2019
Nationality
TurquiaCentre
Telecommunications and MultimediaContacts
+351222094000
serkan.sulun@inesctec.pt
2022
Authors
Sulun, S; Davies, MEP; Viana, P;
Publication
IEEE ACCESS
Abstract
In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.
2021
Authors
Sulun, S; Davies, MEP;
Publication
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
Abstract
In this paper, we address a subtopic of the broad domain of audio enhancement, namely musical audio bandwidth extension. We formulate the bandwidth extension problem using deep neural networks, where a band-limited signal is provided as input to the network, with the goal of reconstructing a full-bandwidth output. Our main contribution centers on the impact of the choice of low-pass filter when training and subsequently testing the network. For two different state-of-the-art deep architectures, ResNet and U-Net, we demonstrate that when the training and testing filters are matched, improvements in signal-to-noise ratio (SNR) of up to 7 dB can be obtained. However, when these filters differ, the improvement falls considerably and under some training conditions results in a lower SNR than the band-limited input. To circumvent this apparent overfitting to filter shape, we propose a data augmentation strategy which utilizes multiple low-pass filters during training and leads to improved generalization to unseen filtering conditions at test time.
2020
Authors
Sulun, S; Tekalp, AM;
Publication
Signal, Image and Video Processing
Abstract
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