SPATIAL SWARM GRANULATION

This paper presents an implementation for dynamic two or three dimensional spatial distribution of granulated sound (or granular synthesis) over an arbitrary loudspeaker system

Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naïve PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO).The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Self-Organised Music

Self-organisation, as manifest, for example, by swarms, flock, herds and other collectives, is a powerful natural force, capable of generating large and sustained structures. Yet the individuals who participate in these social groups may not even be aware of the structures that they are creating. Almost certainly, these structures emerge through the application of simple, local interactions. Improvised music is an uncertain activity, characterised by a lack of top-down organisation and busy, local activity between improvisers. Emerging structures may only be perceivable at a (temporal) distance. The development of higher-level musical structure arises from interactions at lower levels, and we propose here that the self-organisation of social animals provides a very suggestive analogy. This paper builds a model of interactivity based on stigmergy, the process by which social insects communicate indirectly by environment modification. The improvisational element of our model arises from the dynamics of a particle swarm. A process called interpretation extracts musical parameters from the aural sound environment, and uses these parameters to place attractors in the environment of the swarm, after which stigmergy can take place. The particle positions are reinterpreted as parameterised audio events. This paper describes this model and two applications, Swarm Music and Swarm Granulator

Multi-point nonlinear spatial distribution of effects across the soundfield

This paper outlines a method of applying non-linear processing and effects to multi-point spatial distributions of sound spectra. The technique is based on previous research by the author on non-linear spatial distributions of spectra, that is, timbre spatialisation in the frequency domain. One of the primary applications here is the further elaboration of timbre spatialisation in the frequency domain to account for distance cues incorporating loudness attenuation, reverb, and filtration. Further to this, the same approach may also give rise to more non-linear distributions of processing and effects across multi-point spatial distributions such as audio distortions and harmonic exciters, delays, and other such parallel processes used within a spatial context

Pattern Recognition of Surgically Altered Face Images Using Multi-Objective Evolutionary Algorithm

Plastic surgery has been recently coming up with a new and important aspect of face recognition alongside pose, expression, illumination, aging and disguise. Plastic surgery procedures changes the texture, appearance and the shape of different facial regions. Therefore, it is difficult for conventional face recognition algorithms to match a post-surgery face image with a pre-surgery face image. The non-linear variations produced by plastic surgery procedures are hard to be addressed using current face recognition algorithms. The multi-objective evolutionary algorithm is a novel approach for pattern recognition of surgically altered face images. The algorithms starts with generating non-disjoint face granules and two feature extractors EUCLBP (Extended Uniform Circular Local Binary Pattern) and SIFT (Scale Invariant Feature Transform), are used to extract discriminating facial information from face granules. DOI: 10.17762/ijritcc2321-8169.150316

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Spectromorphology and spatiomorphology of sound shapes: Audio-rate AEP and DBAP panning of spectra

Explorations of a new mapping strategy for spectral spatial-isation demonstrate a concise and flexible control of both spatiomorphology and spectromorphology. With the crea-tion of customized software by the author for audio-rate histograms, spectral processing function smoothing, spec-tral centroid width modulation, audio-rate distance-based amplitude panning, audio-rate ambisonic equivalent pan-ning, a growing library of audio trajectory functions, and an assortment of spectral transformation functions, this article tries to explain the rationale of this process

The News Delivery Channel Recommendation Based on Granular Neural Network

With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many real-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various platforms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources