8 citations · 11 across the 6 of their papers we have counts for
11 papers
Understanding Audio Features via Trainable Basis Functions
Kwan Yee Heung, Kin Wai Cheuk, Dorien Herremans
In this paper we explore the possibility of maximizing the information represented in spectrograms by making the spectrogram basis functions trainable. We experiment with two diffe…
Conditional Drums Generation using Compound Word Representations
Dimos Makris, Guo Zixun, Maximos Kaliakatsos-Papakostas +1
The field of automatic music composition has seen great progress in recent years, specifically with the invention of transformer-based architectures. When using any deep learning m…
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses
Phoebe Chua, Dimos Makris, Dorien Herremans +2
Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of…
MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Control
Rui Guo, Ivor Simpson, Chris Kiefer +2
We present a novel music generation framework for music infilling, with a user friendly interface. Infilling refers to the task of generating musical sections given the surrounding…
ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data
Kin Wai Cheuk, Dorien Herremans, Li Su
Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings…
Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework
Dimos Makris, Kat R. Agres, Dorien Herremans
The field of automatic music composition has seen great progress in the last few years, much of which can be attributed to advances in deep neural networks. There are numerous stud…