Writer Identification Using Inexpensive Signal Processing Techniques

We propose to use novel and classical audio and text signal-processing and otherwise techniques for "inexpensive" fast writer identification tasks of scanned hand-written documents "visually". The "inexpensive" refers to the efficiency of the identification process in terms of CPU cycles while preserving decent accuracy for preliminary identification. This is a comparative study of multiple algorithm combinations in a pattern recognition pipeline implemented in Java around an open-source Modular Audio Recognition Framework (MARF) that can do a lot more beyond audio. We present our preliminary experimental findings in such an identification task. We simulate "visual" identification by "looking" at the hand-written document as a whole rather than trying to extract fine-grained features out of it prior classification.Comment: 9 pages; 1 figure; presented at CISSE'09 at http://conference.cisse2009.org/proceedings.aspx ; includes the the application source code; based on MARF described in arXiv:0905.123

MARF: Modular Audio Recognition Framework (in French)

Le Modular Audio Recognition Framework (MARF) concu en 2002, est une plateforme de recherche open-source et une collection de composants avec des algorithmes pour le traitement de la voix, le son, la parole, et l'écriture et de langues naturelles (TALN) MARF a été crée en Java et organisé sous forme de modules extensible qui facilite l'addition de nouvelles algorithmes. MARF peut être utilisé comme une bibliothèque dans un application ou comme une base de support à l'apprentisage et en extension. MARF a aussi été publié dans les plusieurs articles de conférence avec les detailles scientifiques dedant. De la documentation détaillée et la référence d'API en format javadoc sont disponibles étant donné que le projet tente d'être bien-documenté. MARF et ses applications sont déployés sous une licence BSD

Noise Data Visualization and Identification Project

This project aims to produce a space and time map of noise levels within a city using data gathered from sensors, with the goal of identifying noise hot spots and quiet zones. It also includes a noise identification module that attempts to classify reported sound data

MARFL: An Intensional Language for Demand-Driven Management of Machine Learning Backends

Artificial Intelligence (AI) is a rapidly evolving field that has transformed numerous industries and one of its key applications, Pattern Recognition, has been instrumental to the success of Large Language Models like ChatGPT, Bard, etc. However, scripting these advanced systems can be complex and challenging for some users. In this research, we propose a simpler scripting language to perform complex pattern recognition tasks. We introduce a new intensional programming language, MARFL, which is an extension of the Lucid family supported by General Intensional Programming System (GIPSY). Our solution focuses on providing syntax and semantics for MARFL, which enables scripting of Modular A* Recognition Framework (MARF)-based applications as context aware, where the notion of context represents fine-grained configuration details of a given MARF instance. We adapt the concept of context to provide an easily comprehensible language that can perform complex pattern recognition tasks on a demand-driven system such as GIPSY. Our solution is also generic enough to handle other machine learning backends such as PyTorch or TensorFlow in the future. We also provide a complete implementation of our approach, including a new compiler component and MARFL-specific execution engines within GIPSY. Our work extends the use of intensional programming to modeling and executing scripted pattern recognition tasks, which can be used for implementing different algorithmic specifications. Additionally, we utilize the demand-driven distributed computing capabilities of GIPSY to enable an efficient and scalable execution

Noise Data Visualization and Identification Project

This project aims to produce a space and time map of noise levels within a city using data gathered from sensors, with the goal of identifying noise hot spots and quiet zones. It also includes a noise identification module that attempts to classify reported sound data

LifeCLEF Bird Identification Task 2015

International audienceThe LifeCLEF bird identification task provides a testbed for a system-oriented evaluation of 999 bird species identification. The main originality of this data is that it was specifically built through a citizen science initiative conducted by Xeno-Canto, an international social network of amateur and expert ornithologists. This makes the task closer to the conditions of a real-world application than previous, similar initiatives. This overview presents the resources and the assessments of the task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results