José Ramón Beltrán

David Diaz-Guerra, Archontis Politis, Antonio Miguel et al.

Many multi-source localization and tracking models based on neural networks use one or several recurrent layers at their final stages to track the movement of the sources. Conventional recurrent neural networks (RNNs), such as the long short-term memories (LSTMs) or the gated recurrent units (GRUs), take a vector as their input and use another vector to store their state. However, this approach results in the information from all the sources being contained in a single ordered vector, which is not optimal for permutation-invariant problems such as multi-source tracking. In this paper, we present a new recurrent architecture that uses unordered sets to represent both its input and its state and that is invariant to the permutations of the input set and equivariant to the permutations of the state set. Hence, the information of every sound source is represented in an individual embedding and the new estimates are assigned to the tracked trajectories regardless of their order.

Carlos Hernandez-Olivan, Javier Hernandez-Olivan, Jose R. Beltran

Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future.

Carlos Hernandez-Olivan, Jose R. Beltran, David Diaz-Guerra

The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of structural boundaries of the music pieces. This structural boundary analysis has recently been studied with unsupervised methods and \textit{end-to-end} techniques such as Convolutional Neural Networks (CNN) using Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs and trained with human annotations. Several studies have been published divided into unsupervised and \textit{end-to-end} methods in which pre-processing is done in different ways, using different distance metrics and audio characteristics, so a generalized pre-processing method to compute model inputs is missing. The objective of this work is to establish a general method of pre-processing these inputs by comparing the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN in order to establish the most efficient way to extract the limits of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies we obtain a measurement accuracy $F_1$ of 0.411 that outperforms the current one obtained under the same conditions.