scholarly journals Interactive Computation of Timbre Spaces for Sound Synthesis Control

2020 ◽  
pp. 69-78
Author(s):  
Stefano Fasciani
Keyword(s):  
Ad Hoc ◽  
Dimensional Space ◽  
Mapping Method ◽  
Machine Learning Techniques ◽  
Interactive Approach ◽  
Learning Machines ◽  
Interactive Computation ◽  
Machine Listening ◽  
Learning Techniques ◽  
Low Dimensional

Expressive sonic interaction with sound synthesizers requires the control of a continuous and high dimensional space. Further, the relationship between synthesis variables and timbre of the generated sound is typically complex or unknown to users. In previous works, we presented an unsupervised mapping method based on machine listening and machine learning techniques, which addresses these challenges by providing a low-dimensional and perceptually related timbre control space. The mapping maximizes the breadth of the explorable sonic space covered by the sound synthesizer, and minimizes possible timbre losses due to the low­-dimensional control. The mapping is generated automatically by a system requiring little input from users. In this paper we present an improved method and an optimized implementation that drastically reduce the time for timbre analysis and mapping computation. Here we introduce the use of the extreme learning machines for the regression from control to timbre spaces, and an interactive approach for the analysis of the synthesizer sonic response, performed as users explore the parameters of the instrument. This work is implemented in a generic and open-source tool that enables the computation of ad hoc synthesis mappings through timbre spaces, facilitating and speeding up the workflow to get a customized sonic control system.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

Manifold Learning for Medical Image Registration, Segmentation, and Classification

2012 ◽  
pp. 351-372 ◽  
Author(s):  
Paul Aljabar ◽  
Robin Wolz ◽  
Daniel Rueckert
Keyword(s):  
Image Analysis ◽  
Image Registration ◽  
Manifold Learning ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Dimensional Space ◽  
Single Point ◽  
Machine Learning Techniques ◽  
Local Linear Embedding ◽  
Learning Techniques

The term manifold learning encompasses a class of machine learning techniques that convert data from a high to lower dimensional representation while respecting the intrinsic geometry of the data. The intuition underlying the use of manifold learning in the context of image analysis is that, while each image may be viewed as a single point in a very high-dimensional space, a set of such points for a population of images may be well represented by a sub-manifold of the space that is likely to be non-linear and of a significantly lower dimension. Recently, manifold learning techniques have begun to be applied to the field of medical image analysis. This chapter will review the most popular manifold learning techniques such as Multi-Dimensional Scaling (MDS), Isomap, Local linear embedding, and Laplacian eigenmaps. It will also demonstrate how these techniques can be used for image registration, segmentation, and biomarker discovery from medical images.

scholarly journals New metric to quantify the similarity between planetary systems: application to dimensionality reduction using T-SNE

2019 ◽  
Vol 624 ◽  
pp. A45 ◽  
Author(s):  
Y. Alibert
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Space ◽  
Random Systems ◽  
Planetary Systems ◽  
Machine Learning Techniques ◽  
Underlying Structure ◽  
Population Synthesis ◽  
Learning Techniques ◽  
The One ◽  
Protoplanetary Discs

Context. Planet formation models now often consider the formation of planetary systems with more than one planet per system. This raises the question of how to represent planetary systems in a convenient way (e.g. for visualisation purpose) and how to define the similarity between two planetary systems, for example to compare models and observations. Aims. We define a new metric to infer the similarity between two planetary systems, based on the properties of planets that belong to these systems. We then compare the similarity of planetary systems with the similarity of protoplanetary discs in which they form. Methods. We first define a new metric based on mixture of Gaussians, and then use this metric to apply a dimensionality reduction technique in order to represent planetary systems (which should be represented in a high-dimensional space) in a two-dimensional space. This allows us study the structure of a population of planetary systems and its relation with the characteristics of protoplanetary discs in which planetary systems form. Results. We show that the new metric can help to find the underlying structure of populations of planetary systems. In addition, the similarity between planetary systems, as defined in this paper, is correlated with the similarity between the protoplanetary discs in which these systems form. We finally compare the distribution of inter-system distances for a set of observed exoplanets with the distributions obtained from two models: a population synthesis model and a model where planetary systems are constructed by randomly picking synthetic planets. The observed distribution is shown to be closer to the one derived from the population synthesis model than from the random systems. Conclusions. The new metric can be used in a variety of unsupervised machine learning techniques, such as dimensionality reduction and clustering, to understand the results of simulations and compare them with the properties of observed planetary systems.

PATTERNS OF PREPOSITIONAL ATTACHMENTS — WHERE DICTIONARY SEMANTICS MEETS CORPUS STATISTICS

2000 ◽  
Vol 14 (06) ◽  
pp. 809-838 ◽  
Author(s):  
SANDA M. HARABAGIU
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Learning Process ◽  
Wall Street Journal ◽  
Ad Hoc ◽  
Machine Learning Techniques ◽  
Journal Articles ◽  
Wall Street ◽  
Web Documents ◽  
Learning Techniques

This paper presents a novel methodology of disambiguating prepositional phrase attachments. We create patterns of attachments by classifying a collection of prepositional relations derived from Treebank parses. As a by-product, the arguments of every prepositional relation are semantically disambiguated. Attachment decisions are generated as the result of a learning process, that builds upon some of the most popular current statistical and machine learning techniques. We have tested this methodology on (1) Wall Street Journal articles, (2) textual definitions of concepts from a dictionary and (3) an ad hoc corpus of Web documents, used for conceptual indexing and information extraction.

scholarly journals Fault Identification Based on Local Feature Correlation

2022 ◽  
pp. 119-146
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Data Processing ◽  
High Dimension ◽  
Dimensional Space ◽  
Original Data ◽  
Mapping Method ◽  
Kernel Functions ◽  
High Dimensional ◽  
Monitoring Methods ◽  
Multivariate Statistical ◽  
Low Dimensional

AbstractIndustrial data variables show obvious high dimension and strong nonlinear correlation. Traditional multivariate statistical monitoring methods, such as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space through appropriate kernel functions so as to achieve the goal of linear separability in the new space. However, the space projection from the low dimension to the high dimension is contradictory to the actual requirement of dimensionality reduction of the data. So kernel-based method inevitably increases the complexity of data processing.

scholarly journals Linguistic Landscapes on Street-Level Images

2020 ◽  
Vol 9 (1) ◽  
pp. 57
Author(s):  
Seong-Yun Hong
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Machine Learning Techniques ◽  
Spatiotemporal Pattern ◽  
Linguistic Landscape ◽  
Social Changes ◽  
Street Level ◽  
Learning Techniques ◽  
Landscape Research ◽  
Objective Description

Linguistic landscape research focuses on relationships between written languages in public spaces and the sociodemographic structure of a city. While a great deal of work has been done on the evaluation of linguistic landscapes in different cities, most of the studies are based on ad-hoc interpretation of data collected from fieldwork. The purpose of this paper is to develop a new methodological framework that combines computer vision and machine learning techniques for assessing the diversity of languages from street-level images. As demonstrated with an analysis of a small Chinese community in Seoul, South Korea, the proposed approach can reveal the spatiotemporal pattern of linguistic variations effectively and provide insights into the demographic composition as well as social changes in the neighborhood. Although the method presented in this work is at a conceptual stage, it has the potential to open new opportunities to conduct linguistic landscape research at a large scale and in a reproducible manner. It is also capable of yielding a more objective description of a linguistic landscape than arbitrary classification and interpretation of on-site observations. The proposed approach can be a new direction for the study of linguistic landscapes that builds upon urban analytics methodology, and it will help both geographers and sociolinguists explore and understand our society.

Generation of Random Fields for Image Segmentation Using Manifold Learning Technique

2016 ◽  
Vol 30 (10) ◽  
pp. 1654007 ◽  
Author(s):  
Rambabu Pemula ◽  
C. Nagaraju
Keyword(s):  
Image Segmentation ◽  
Manifold Learning ◽  
Random Fields ◽  
Dimensional Space ◽  
Single Point ◽  
Machine Learning Techniques ◽  
Intrinsic Geometry ◽  
Local Linear Embedding ◽  
Learning Techniques ◽  
Learning Technique

The manifold learning technique is a class of machine learning techniques that converts the intrinsic geometry of the data from higher to lower dimensional representation. The manifold learning technique in image analysis is used to view as a single point in a very high-dimensional space, a set of such points for population of images that may be well represented by a sub-manifold of the space that is likely to be nonlinear and of significantly lower dimensions. In this paper, we presented a new method to generate optimal random fields for image segmentation using local linear embedding manifold learning technique. This method gives better segmentation results compared to entropy classification and occlusion reasoning techniques.

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

2021 ◽  
pp. 036119812110520
Author(s):  
Fiorella Mete ◽  
David J. Corr ◽  
Michael P. Wilbur ◽  
Ying Chen
Keyword(s):  
Machine Learning ◽  
Ad Hoc ◽  
Learning Algorithm ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Classification Framework ◽  
Learning Techniques ◽  
Tree Methods ◽  
Step Algorithm

Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

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