Waldo Fajardo

Inmamusys: Intelligent multiagent music system

Music generation is a complex task even for human beings. This paper describes a two level competitive/collaborative multiagent approach for autonomous, non-deterministic, computer music composition. Our aim is to build a high modular system that composes music on its own by using Experts Systems technology and rule-based systems principles. To do that, rules issued from musical knowledge are used and emotional inputs from the users are introduced. In fact, users are not allowed to directly control the composition process. Two main goals are sought after: investigating relationships between computers and emotions and how the latter can be represented into the former, and developing a framework for music composition that can be useful for future experiments. The system has been successfully tested by asking several people to match compositions with suggested emotions.

DrugNet: Network-based drug-disease prioritization by integrating heterogeneous data

OBJECTIVE Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process. METHODS We have developed DrugNet, a new methodology for drug-disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning. RESULTS We tested the performance of our approach on different validation tests, including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552±0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0-4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem. CONCLUSIONS Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website.

A state of the art on computational music performance

Musical expressivity can be defined as the deviation from a musical standard when a score is performed by a musician. This deviation is made in terms of intrinsic note attributes like pitch, timbre, timing and dynamics. The advances in computational power capabilities and digital sound synthesis have allowed real-time control of synthesized sounds. Expressive control becomes then an area of great interest in the sound and music computing field. Musical expressivity can be approached from different perspectives. One approach is the musicological analysis of music and the study of the different stylistic schools. This approach provides a valuable understanding about musical expressivity. Another perspective is the computational modelling of music performance by means of automatic analysis of recordings. It is known that music performance is a complex activity that involves complementary aspects from other disciplines such as psychology and acoustics. It requires creativity and eventually, some manual abilities, being a hard task even for humans. Therefore, using machines appears as a very interesting and fascinating issue. In this paper, we present an overall view of the works many researchers have done so far in the field of expressive music performance, with special attention to the computational approach.

Mining transposed motifs in music

The discovery of frequent musical patterns (motifs) is a relevant problem in musicology. This paper introduces an unsupervised algorithm to address this problem in symbolically-represented musical melodies. Our algorithm is able to identify transposed patterns including exact matchings, i.e., null transpositions. We have tested our algorithm on a corpus of songs and the results suggest that our approach is promising, specially when dealing with songs that include non-exact repetitions.

BioMen: an information system to herbarium

This paper presents the development of BioMen (Biological Management Executed over Network), an Internet-managed system. By using service ontologies, the user is able to perform services remotely from a web browser. The services are managed by means of a Multi-Agent System, i.e. an Input/Output system, which interacts with the web server. In addition, artificial intelligence techniques have been incorporated so that the necessary information may be obtained for the study of biodiversity. We have built a tool which will be of particular use to botanists and which can by accessed from anywhere in the world thanks to Internet technology. In this paper, we shall present the problems we encountered when building this tool and how we managed to overcome them.

Representation model and learning algorithm for uncertain and imprecise multivariate behaviors, based on correlated trends

Graphical abstractDisplay Omitted HighlightsRepresentation model for uncertain and imprecise multivariate dataseries.Basic idea: finding repeating frequent correlated patterns among the different dimensions of the dataseries.Deal with data imperfection directly, not transforming the data and pretending it has no imperfection.Applicable to several problems that can be represented by series of observations.Provide a fix size representation, regardless of the length of the dataseries. The computational representation and classification of behaviors is a task of growing interest in the field of Behavior Informatics, being series of data a common way of describing those behaviors. However, as these data are often imperfect, new representation models are required in order to effectively handle imperfection in this context. This work presents a new approach, Frequent Correlated Trends, for representing uncertain and imprecise multivariate data series. Such a model can be applied to any domain where behaviors recur in similar-but not identical-shape. In particular, we have already used them to the task of identifying the performers of violin recordings with good results. The present paper describes the abstract model representation and a general learning algorithm, and discusses several potential applications.

Transcribing Debussy's Syrinx dynamics through Linguistic Description

Advances in computational power have enabled the manipulation of music audio files and the emergence of the Music Information Retrieval field. One of the main research lines in this area is that of Music Transcription, which aims at transforming an audio file into musical notation. So far, most efforts have focused on accurately transcribing the pitch and durations of the notes, and thus neglecting other aspects in the music score. The present work explores a novel line of action in the context of automatic music transcription, focusing on the dynamics, and by means of Linguistic Description. The process described in this paper (called MUDELD: MUsic Dynamics Extraction through Linguistic Description) departs from the data series representing the audio file, and requires the segmentation of the piece in phrases, which is currently done by hand. Initial experiments have been performed on eight recordings of Debussys Syrinx with promising results.

Continuous classifying associative memory

In this article we present the so‐called continuous classifying associative memory, able to store continuous patterns avoiding the problems of spurious states and data dependency. This is a memory model based on our previously developed classifying associative memory, which enables continuous patterns to be stored and recovered. We will also show that the behavior of this continuous classifying associative memory may be adjusted to some predetermined goals by selecting some internal operating functions.

CLAM: A New Model of Associative Memory

We present a new associative memory model that stores arbitrary bipolar patterns without the problems we can find in other models like BAM or LAM. After identifying those problems we show the new memory topology and we explain its learning and recall stages. Mathematical demonstrations are provided to prove that the new memory model guarantees the perfect retrieval of every stored pattern and also to prove that whatever the input of the memory is, it operates as a nearest neighbor classifier. ©2000 John Wiley & Sons, Inc.

Mining Musical Patterns: Identification of Transposed Motives

Automatic extraction of frequent repeated patterns in music material is an interesting problem. This paper presents an effective approach of unsupervised frequent pattern discovery method from symbolic music sources. Patterns are discovered even if they are transposed. Experiments on some songs suggest that our approach is promising, specially when dealing with songs that include non-exact repetitions.