@conference {2022,
	title = {Conditional Drums Generation using Compound Word Representations},
	booktitle = {EvoMUSART (EVO*) - Lecture Notes in Computer Science},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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 model which considers music as a sequence of events with multiple complex dependencies, the selection of a proper data representation is crucial. In this paper, we tackle the task of conditional drums generation using a novel data encoding scheme inspired by the Compound Word representation, a tokenization process of sequential data. Therefore, we present a sequence-to-sequence architecture where a Bidirectional Long short-term memory (BiLSTM) Encoder receives information about the conditioning parameters (i.e., accompanying tracks and musical attributes), while a Transformer-based Decoder with relative global attention produces the generated drum sequences. We conducted experiments to thoroughly compare the effectiveness of our method to several baselines. Quantitative evaluation shows that our model is able to generate drums sequences that have similar statistical distributions and characteristics to the training corpus. These features include syncopation, compression ratio, and symmetry among others. We also verified, through a listening test, that generated drum sequences sound pleasant, natural and coherent while they {\textquoteleft}{\textquoteleft}groove{\textquoteright}{\textquoteright} with the given accompaniment.},
	author = {D. Makris and Zixun Guo and N. Kaliakatsos-Papakostas and D. Herremans}
}
@conference {2022,
	title = {A Machine Learning Approach for MIDI to Guitar Tablature Conversion},
	booktitle = {Sound and Music Computing Conference (SMC)},
	year = {2022},
	author = {N. Kaliakatsos-Papakostas and G. Bastas and D. Makris and D. Herremans and V. Katsouros and P. Maragos}
}
@article {2022,
	title = {Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses},
	journal = {Arxiv preprint},
	year = {2022},
	abstract = {Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.},
	url = {https://arxiv.org/abs/2202.10453},
	author = {P. Chua and D. Makris and K. Agres and G. Roig and D. Herremans}
}
@conference {2021,
	title = {Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Virtual},
	url = {https://arxiv.org/abs/2104.13056},
	author = {D. Makris and K. Agres and D. Herremans}
}
@conference {2021,
	title = {Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {Proceedings of IJCNN},
	organization = {Proceedings of IJCNN},
	address = {Online},
	abstract = {The rise of deep learning technologies has quickly advanced many fields, including that of generative music systems. There exist a number of systems that allow for the generation of good sounding short snippets, yet, these generated snippets often lack an overarching, longer-term structure. In this work, we propose CM-HRNN: a conditional melody generation model based on a hierarchical recurrent neural network. This model allows us to generate melodies with long-term structures based on given chord accompaniments. We also propose a novel, concise event-based representation to encode musical lead sheets while retaining the notes{\textquoteright} relative position within the bar with respect to the musical meter. With this new data representation, the proposed architecture can simultaneously model the rhythmic, as well as the pitch structures in an effective way. Melodies generated by the proposed model were extensively evaluated in quantitative experiments as well as a user study to ensure the musical quality of the output as well as to evaluate if they contain repeating patterns. We also compared the system with the state-of-the-art AttentionRNN. This comparison shows that melodies generated by CM-HRNN contain more repeated patterns (i.e., higher compression ratio) and a lower tonal tension (i.e., more tonally concise). Results from our listening test indicate that CM-HRNN outperforms AttentionRNN in terms of long-term structure and overall rating.},
	url = {https://arxiv.org/abs/2102.09794},
	author = {Zixun Guo and D. Makris and D. Herremans}
}
