Large scale musical instrument identification

In this paper, automatic musical instrument identification using a variety of classifiers is addressed. Experiments are performed on a large set of recordings that stem from 20 instrument classes. Several features from general audio data classification applications as well as MPEG-7 descriptors are measured for 1000 recordings. Branch-and-bound feature selection is applied in order to select the most discriminating features for instrument classification. The first classifier is based on non-negative matrix factorization (NMF) techniques, where training is performed for each audio class individually. A novel NMF testing method is proposed, where each recording is projected onto several training matrices, which have been Gram-Schmidt orthogonalized. Several NMF variants are utilized besides the standard NMF method, such as the local NMF and the sparse NMF. In addition, 3-layered multilayer perceptrons, normalized Gaussian radial basis function networks, and support vector machines employing a polynomial kernel have also been tested as classifiers. The classification accuracy is high, ranging between 88.7% to 95.3%, outperforming the state-of-the-art techniques tested in the aforementioned experiment

Расчет теплопереноса в наноразмерных гетероструктурах

Abstract. The article discusses the calculation of the temperature regime in nanoscale AlAs/GaAs binary heterostructures. When modeling heat transfer in nanocomposites, it is important to take into account that heat dissipation in multilayer structures with layer sizes of the order of the mean free path of energy carriers (phonons and electrons) occurs not at the lattice, but at the layer boundaries (interfaces). In this regard, the use of classical numerical models based on the Fourier law is limited, because it gives significant errors. To obtain more accurate results, we used a model in which the heat distribution was assumed to be constant inside the layer, while the temperature was stepwise changed at the interfaces of the layers. A hybrid approach was used for the calculation: a finite−difference method with an implicit scheme for time approximation and a mesh−free model based on a set of radial basis functions for spatial approximation. The calculation of the parameters of the bases was carried out through the solution of the systems of linear algebraic equations. In this case, only weights of neuroelements were selected, and the centers and «widths» were fixed. As an approximator, a set of frequently used basic functions was considered. To increase the speed of calculations, the algorithm was parallelized. Calculation times were measured to estimate the performance gains using the parallel implementation of the method.Аннотация. Проведен расчет температурного режима в наноразмерных бинарных гетероструктурах AlAs/ GaAs. При моделировании теплопереноса в нанокомпозитах важно учитывать, что рассеивание тепла в многослойных структурах при размерах слоев порядка длины свободного пробега носителей энергии (фононов и электронов) происходит не на кристаллической решетке, а на границах слоев (интерфейсах). Поэтому использование классических численных моделей, основанных на законе Фурье, сильно ограничено, так как дает существенные погрешности. Для получения более точных результатов. Использована модель, в которой распределение тепла предполагалось постоянным внутри слоя, при этом температура ступенчато изменялась на интерфейсах слоев. Для вычисления использован гибридный подход: конечно−разностный метод с неявной схемой для временной аппроксимации и бессеточная модель на основе набора радиально− базисных функций для пространственной аппроксимации. Расчет параметров базисов проведен через решение системы линейных алгебраических уравнений. При этом подбирали только весовые коэффициенты нейроэлементов, а центры и «ширины» были фиксированы. В качестве аппроксиматоров рассмотрен набор часто используемых базисных функций. Для увеличения скорости вычислений выполнена параллелизация алгоритма. Проведены замеры времени счета для оценки прироста производительности при использовании параллельной реализации метода

Вычисление эффективного коэффициента теплопроводности сверхрешетки на основе кинетического уравнения Больцмана с использованием первопринципных расчетов

In this work, we calculate the effective thermal conductivity coefficient for a binary semiconductor heterostructure using the GaAs/AlAs superlattice as an example. Different periods of layers and different ambient temperatures are considered. At the scale under consideration, the use of models based on the Fourier law is very limited, since they do not take into account the quantum-mechanical properties of materials, which gives a strong discrepancy with experimental data. On the other hand, the use of molecular dynamics methods allows us to obtain accurate solutions, but they are significantly more demanding on computing resources and also require solving a non-trivial problem of potential selection. When considering nanostructures, good results were shown by methods based on the solution of the Boltzmann transport equation for phonons; they allow one to obtain a fairly accurate solution, while having less computational complexity than molecular dynamics methods. To calculate the thermal conductivity coefficient, a modal suppression model is used that approximates the solution of the Boltzmann transport equation for phonons. The dispersion parameters and phonon scattering parameters are obtained from first-principle calculations. The work takes into account 2-phonon (associated with isotopic disorder and barriers) and 3-phonon scattering processes. To increase the accuracy of calculations, the non-digital profile of the distribution of materials among the layers of the superlattice is taken into account. The obtained results are compared with experimental data showing good agreement.В работе проводится вычисление эффективного коэффициента теплопроводности для бинарной полупроводниковой гетероструктуры на примере сверхрешетки GaAs/AlAs для различных периодов слоев и при различных температурах окружающей среды. На рассматриваемых масштабах использование моделей, основанных на законе Фурье, сильно ограничено, т. к. они не учитывают квантовомеханические свойства материалов, что дает сильное расхождение с экспериментальными данными. С другой стороны, использование методов молекулярной динамики позволяет получить точные решения, но они существенно более требовательны к вычислительным ресурсам и требуют решение нетривиальной задачи подбора потенциала. При рассмотрении наноструктур хорошие результаты показали методы, основанные на решении кинетического уравнения Больцмана для фононов, они позволяют получить достаточно точное решение, при этом обладая меньшей вычислительной сложностью, чем методы молекулярной динамики. Для расчета коэффициента теплопроводности в работе используется модель модального подавления, аппроксимирующая решение кинетического уравнения Больцмана для фононов. Дисперсионные параметры и параметры рассеяния фононов получены из первопринципных расчетов. В работе учитываются двух фононные, связанные с изотопичеким беспорядком и барьерные, и трех фононные процессы рассеяния. Для повышения точности вычислений, в работе учитывается неоднородность распределения материалов по слоям сверхрешетки. Проведено сравнение полученных результатов с экспериментальными данными, продемонстрировано хорошее соответствие

Bioactive molecule prediction using extreme gradient boosting

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets

Steered mixture-of-experts for light field images and video : representation and coding

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution

Numerical Non-Linear Modelling Algorithm Using Radial Kernels on Local Mesh Support

Estimation problems are frequent in several fields such as engineering, economics, and physics, etc. Linear and non-linear regression are powerful techniques based on optimizing an error defined over a dataset. Although they have a strong theoretical background, the need of supposing an analytical expression sometimes makes them impractical. Consequently, a group of other approaches and methodologies are available, from neural networks to random forest, etc. This work presents a new methodology to increase the number of available numerical techniques and corresponds to a natural evolution of the previous algorithms for regression based on finite elements developed by the authors improving the computational behavior and allowing the study of problems with a greater number of points. It possesses an interesting characteristic: Its direct and clear geometrical meaning. The modelling problem is presented from the point of view of the statistical analysis of the data noise considered as a random field. The goodness of fit of the generated models has been tested and compared with some other methodologies validating the results with some experimental campaigns obtained from bibliography in the engineering field, showing good approximation. In addition, a small variation on the data estimation algorithm allows studying overfitting in a model, that it is a problematic fact when numerical methods are used to model experimental values.This research has been partially funded by the Spanish Ministry of Science, Innovation and Universities, grant number RTI2018-101148-B-I00

Deep Radial-Basis Value Functions for Continuous Control

A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-basis value functions (RBVFs): value functions learned using a deep network with a radial-basis function (RBF) output layer. We show that the maximum action-value with respect to a deep RBVF can be approximated easily and accurately. Moreover, deep RBVFs can represent any true value function owing to their support for universal function approximation. We extend the standard DQN algorithm to continuous control by endowing the agent with a deep RBVF. We show that the resultant agent, called RBF-DQN, significantly outperforms value-function-only baselines, and is competitive with state-of-the-art actor-critic algorithms.Comment: In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI

Spiking neural network model of sound localisation using the interaural intensity difference

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise