The multiple voices of musical emotions: source separation for improving music emotion recognition models and their interpretability

Despite the manifold developments in music emotion recognition and related areas, estimating the emotional impact of music still poses many challenges. These are often associated to the complexity of the acoustic codes to emotion and the lack of large amounts of data with robust golden standards. In this paper, we propose a new computational model (EmoMucs) that considers the role of different musical voices in the prediction of the emotions induced by music. We combine source separation algorithms for breaking up music signals into independent song elements (vocals, bass, drums, other) and end-to-end state-of-the-art machine learning techniques for feature extraction and emotion modelling (valence and arousal regression). Through a series of computational experiments on a benchmark dataset using source-specialised models trained independently and different fusion strategies, we demonstrate that EmoMucs outperforms state-of-the-art approaches with the advantage of providing insights into the relative contribution of different musical elements to the emotions perceived by listeners

Modelling long- and short-term structure in symbolic music with attention and recurrence

The automatic composition of music with long-term structure is a central problem in music generation. Neural network-based models have been shown to perform relatively well in melody generation, but generating music with long-term structure is still a major challenge. This paper introduces a new approach for music modelling that combines recent advancements of transformer models with recurrent networks – the long-short term universal transformer (LSTUT), and compare its ability to predict music against current state-of-the-art music models. Our experiments are designed to push the boundaries of music models on considerably long music sequences – a crucial requirement for learning long-term structure effectively. Results show that the LSTUT outperforms all the other models and can potentially learn features related to music structure at different time scales. Overall, we show the importance of integrating both recurrence and attention in the architecture of music models, and their potential use in future automatic composition systems

SOLIS: Autonomous Solubility Screening using Deep Neural Networks

Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and higher throughput. However, it is common that sample preparation is still carried out manually. This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data. Crystallisation experiments are common in many chemical fields, both for purification and in polymorph screening experiments. The initial step often involves a solubility screen of the molecule; that is, understanding whether molecular compounds have dissolved in a particular solvent. This usually can be time consuming and work intensive. Moreover, accurate knowledge of the precise solubility limit of the molecule is often not required, and simply measuring a threshold of solubility in each solvent would be sufficient. To address this, we propose a novel cascaded deep model that is inspired by how a human chemist would visually assess a sample to determine whether the solid has completely dissolved in the solution. In this paper, we design, develop, and evaluate the first fully autonomous solubility screening framework, which leverages state-of-the-art methods for image segmentation and convolutional neural networks for image classification. To realise that, we first create a dataset comprising different molecules and solvents, which is collected in a real-world chemistry laboratory. We then evaluated our method on the data recorded through an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator, and show that our model can achieve 99.13% test accuracy across various setups.Comment: 7 pages, 4 figure

Semantic Integration of MIR Datasets with the Polifonia Ontology Network

Integration between different data formats, and between data belonging to different collections, is an ongoing challenge in the MIR field. Semantic Web tools have proved to be promising resources for making different types of music information interoperable. However, the use of these technologies has so far been limited and scattered in the field. To address this, the Polifonia project is developing an ontological ecosystem that can cover a wide variety of musical aspects (musical features, instruments, emotions, performances). In this paper, we present the Polifonia Ontology Network, an ecosystem that enables and fosters the transition towards semantic MIR