Deep neural networks (DNN) have been successfully applied for music classification tasks including music tagging. In this paper, we investigate the effect of audio preprocessing on music tagging with neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting and scalin...

XML addresses several HTML shortcomings, but its underdefined processing impedes the development of adequate generic presentation models for the Web. Such models must define the parsing, validation, transformation and rendering of multi-language XML documents, according to a variety of adaptation requirements. However, most existing approaches only define subsets of this functionality and do no...

The aim of this paper is to review existing approaches of preprocessing in mammographic images. The objective of preprocessing is to improve the quality of the image and make it ready for further processing by removing the irrelevant noise and unwanted parts in the background of the mammogram. There are different of methods of preprocessing a mammogram image. Their advantages and disadvantages ...

Contraction Hierarchies is a successful speedup-technique to Dijkstra’s seminal shortest path algorithm that has a convenient trade-off between preprocessing and query times. We investigate a shared-memory parallel implementation that uses O(n + m) space for storing the graph and O(1) space for each core during preprocessing. The presented data structures and algorithms consequently exploits ca...

Several resource recovery projects financed in the last 5 years incorporate preprocessing systems for fuel preparation and materials recovery prior to combus­ tion. These projects represent a return to a system of resource recovery which experienced many technical problems in several projects built in the late 1970s and early 1980s. The purpose of this paper is to review the history of solid wa...

Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a metho...

Data preprocessing is a fundamental part of any machine learning application and frequently the most time-consuming aspect when developing a machine learning solution. Preprocessing for deep learning is characterized by pipelines that lazily load data and perform data transformation, augmentation, batching and logging. Many of these functions are common across applications but require different...

Solvers for the Maximum satisfiability (MaxSAT) problem find an increasing number of applications today. We focus on improving MaxHS—one of the most successful recent MaxSAT algorithms— via SAT-based preprocessing. We show that employing SAT-based preprocessing via the so-called labelled CNF (LCNF) framework before calling MaxHS can in some cases greatly degrade the performance of the solver. A...

Text preprocessing plays an important role in a textto-speech (TTS) synthesis system. The correct detection and interpretation of input strings influence the overall system accuracy and contribute to the conversion of an unrestricted text into synthetic speech. This paper describes the design philosophy of a preprocessing module for a TTS system in Romanian language. The preprocessor is impleme...

Given a pattern string P and a text string T , the one-dimensional real-scale pattern matching problem asks for all matched positions in T at which P occurs for some real scales ≥ 1. This problem was first proposed by Amir et al., who also gave an algorithm with O(n + |P |) time for solving it, where |T | = n. Recently, Wang et al. proposed a preprocessing on T with O(n) time and space, with wh...