• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 8
  • 5
  • 2
  • 2
  • 2
  • 1
  • Tagged with
  • 20
  • 20
  • 9
  • 8
  • 8
  • 5
  • 5
  • 5
  • 5
  • 5
  • 4
  • 4
  • 4
  • 4
  • 3
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

Outomatiese genreklassifikasie vir hulpbronskaars tale / Dirk Snyman

Snyman, Dirk Petrus January 2012 (has links)
When working in the terrain of text processing, metadata about a particular text plays an important role. Metadata is often generated using automatic text classification systems which classifies a text into one or more predefined classes or categories based on its contents. One of the dimensions by which a text can be can be classified, is the genre of a text. In this study the development of an automatic genre classification system in a resource scarce environment is postulated. This study aims to: i) investigate the techniques and approaches that are generally used for automatic genre classification systems, and identify the best approach for Afrikaans (a resource scarce language), ii) transfer this approach to other indigenous South African resource scarce languages, and iii) investigate the effectiveness of technology recycling for closely related languages in a resource scarce environment. To achieve the first goal, five machine learning approaches were identified from the literature that are generally used for text classification, together with five common approaches to feature extraction. Two different approaches to the identification of genre classes are presented. The machine learning-, feature extraction- and genre class identification approaches were used in a series of experiments to identify the best approach for genre classification for a resource scarce language. The best combination is identified as the multinomial naïve Bayes algorithm, using a bag of words approach as features to classify texts into three abstract classes. This results in an f-score (performance measure) of 0.929 and it was subsequently shown that this approach can be successfully applied to other indigenous South African languages. To investigate the viability of technology recycling for genre classification systems for closely related languages, Dutch test data was classified using an Afrikaans genre classification system and it is shown that this approach works well. A pre-processing step was implemented by using a machine translation system to increase the compatibility between Afrikaans and Dutch by translating the Dutch texts before classification. This results in an f-score of 0.577, indicating that technology recycling between closely related languages has merit. This approach can be used to promote and fast track the development of genre classification systems in a resource scarce environment. / MA (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2013
12

Audio content processing for automatic music genre classification : descriptors, databases, and classifiers

Guaus, Enric 21 September 2009 (has links)
Aquesta tesi versa sobre la classificació automàtica de gèneres musicals, basada en l'anàlisi del contingut del senyal d'àudio, plantejant-ne els problemes i proposant solucions. Es proposa un estudi de la classificació de gèneres musicals des del punt de vista computacional però inspirat en teories dels camps de la musicologia i de la percepció. D'aquesta manera, els experiments presentats combinen diferents elements que influeixen en l'encert o fracàs de la classificació, com ara els descriptors d'àudio, les tècniques d'aprenentatge, etc. L'objectiu és avaluar i comparar els resultats obtinguts d'aquests experiments per tal d'explicar els límits d'encert dels algorismes actuals, i proposar noves estratègies per tal de superar-los. A més a més, partint del processat de la informació d'àudio, s'inclouen aspectes musicals i culturals referents al gènere que tradicionalment no han estat tinguts en compte en els estudis existents. En aquest context, es proposa l'estudi de diferents famílies de descriptors d'àudio referents al timbre, ritme, tonalitat o altres aspectes de la música. Alguns d'aquests descriptors són proposats pel propi autor mentre que d'altres ja són perfectament coneguts. D'altra banda, també es comparen les tècniques d'aprenentatge artificial que s'usen tradicionalment en aquest camp i s'analitza el seu comportament davant el nostre problema de classificació. També es presenta una discussió sobre la seva capacitat per representar els diferents models de classificació proposats en el camp de la percepció. Els resultats de la classificació es comparen amb un seguit de tests i enquestes realitzades sobre un conjunt d'individus. Com a resultat d'aquesta comparativa es proposa una arquitectura específica de classificadors que també està raonada i explicada en detall. Finalment, es fa un especial èmfasi en comparar resultats dels classificadors automàtics en diferents escenaris que pressuposen la barreja de bases de dades, la comparació entre bases de dades grans i petites, etc. A títol de conclusió, es mostra com l'arquitectura de classificació proposada, justificada pels resultats dels diferents anàlisis, pot trencar el límit actual en tasques de classificació automàtica de gèneres musicals. De manera condensada, es pot dir que aquesta tesi contribueix al camp de la classificació de gèneres musicals en els següents aspectes: a) Proporciona una revisió multidisciplinar delsgèneres musicals i la seva classificació; b)Presenta una avaluació qualitativa i quantitativa de les famílies de descriptors d'àudio davant el problema de la classificació de gèneres; c) Avalua els pros i contres de les diferents tècniques d'aprenentatge artificial davant el gènere; d) Proposa una arquitectura nova de classificador d'acord amb una visió interdisciplinar dels gèneres musicals; e) Analitza el comportament de l'arquitecturaproposada davant d'entorns molt diversos en el que es podria implementar el classificador. / Esta tesis estudia la clasificación automática degéneros musicales, basada en el análisis delcontenido de la señal de audio, planteando sus problemas y proponiendo soluciones. Sepropone un estudio de la clasificación de los géneros musicales desde el punto de vista computacional, pero inspirado en teorías de los campos de la musicología y la percepción. De este modo, los experimentos persentados combinan distintos elementos que influyen en el acierto o fracaso de la clasificación, como por ejemplo los descriptores de audio, las técnicas de aprondiza je, etc. El objetivo es comparar y evaluar los resultados obtenidos de estos experimentos para explicar los límites de las tasas de acierto de los algorismos actuales, y proponer nuevas estrategias para superarlos. Además, partiendo del procesado de la información de Audio, se han incluido aspectos musicales y culturales al género que tradicionalmente no han sido tomados en cuenta en los estudios existentes. En este contexto, se propone el estudio de distintas famílias de descriptores de audio referentes al timbre, al ritmo, a la tonalidad o a otros aspectos de la música. Algunos de los descriptores son propuestos por el mismo autor, mientras que otros son perfectamente conocidos. Por otra parte, también se comparan las técnicas de aprendiza je artificial que se usan tradicionalmente, y analizamos su comportamiento en frente de nuestro problema de clasificación. Tambien planteamos una discusión sobre su capacidad para representar los diferentes modelos de clasificación propuestos en el campo de la percepción. Estos resultados de la clasificación se comparan con los resultados de unos tests y encuestas realizados sobre un conjunto de individuos. Como resultado de esta comparativa se propone una arquitectura específica de clasificadores que tambien está razonada y detallada en el cuerpo de la tesis. Finalmente, se hace un émfasis especial en comparar los resultados de los clasificadores automáticos en distintos escenarios que assumen la mezcla de bases de datos, algunas muy grandes y otras muy pequeñas, etc. Como conclusión, mostraremos como la arquitectura de clasificación propuesta permite romper el límite actual en el ámbito de la classificación automática de géneros musicales.De forma condensada, se puede decir que esta tesis contribuye en el campo de la clasificación de los géneros musicales el los siguientes aspectos: a) Proporciona una revisión multidisciplinar de los géneros musicales y su clasificación; b) Presenta una evaluación cualitativa y cuantitativa de las famílias de descriptores de audio para la clasificación de géneros musicales; c) Evalua los pros y contras de las distintas técnicas de aprendiza je artificial delante del género; d) Propone una arquitectura nueva del clasificador de acuerdo con una visión interdisciplinar de los géneros musicales; e) Analiza el comportamiento de la arquitectura propuesta delante de entornos muy diversos en los que se podria implementar el clasificador. / This dissertation presents, discusses, and sheds some light on the problems that appear when computers try to automatically classify musical genres from audio signals. In particular, a method is proposed for the automatic music genre classification by using a computational approach that is inspired in music cognition and musicology in addition to Music Information Retrieval techniques. In this context, we design a set of experiments by combining the different elements that may affect the accuracy in the classification (audio descriptors, machine learning algorithms, etc.). We evaluate, compare and analyze the obtained results in order to explain the existing glass-ceiling in genre classification, and propose new strategies to overcome it. Moreover, starting from the polyphonic audio content processing we include musical and cultural aspects of musical genre that have usually been neglected in the current state of the art approaches. This work studies different families of audio descriptors related to timbre, rhythm, tonality and other facets of music, which have not been frequently addressed in the literature. Some of these descriptors are proposed by the author and others come from previous existing studies. We also compare machine learning techniques commonly used for classification and analyze how they can deal with the genre classification problem. We also present a discussion on their ability to represent the different classification models proposed in cognitive science. Moreover, the classification results using the machine learning techniques are contrasted with the results of some listening experiments proposed. This comparison drive us to think of a specific architecture of classifiers that will be justified and described in detail. It is also one of the objectives of this dissertation to compare results under different data configurations, that is, using different datasets, mixing them and reproducing some real scenarios in which genre classifiers could be used (huge datasets). As a conclusion, we discuss how the classification architecture here proposed can break the existing glass-ceiling effect in automatic genre classification. To sum up, this dissertation contributes to the field of automatic genre classification: a) It provides a multidisciplinary review of musical genres and its classification; b) It provides a qualitative and quantitative evaluation of families of audio descriptors used for automatic classification; c) It evaluates different machine learning techniques and their pros and cons in the context of genre classification; d) It proposes a new architecture of classifiers after analyzing music genre classification from different disciplines; e) It analyzes the behavior of this proposed architecture in different environments consisting of huge or mixed datasets.
13

Classificação automática de gênero musical baseada em entropia e fractais / Automatic music genre classification based on entropy and fractals

Antonio José Homsi Goulart 16 February 2012 (has links)
A classificação automática de gênero musical tem como finalidade o conforto de ouvintes de músicas auxiliando no gerenciamento das coleções de músicas digitais. Existem sistemas que se baseiam em cabeçalhos de metadados (tais como nome de artista, gênero cadastrado, etc.) e também os que extraem parâmetros dos arquivos de música para a realização da tarefa. Enquanto a maioria dos trabalhos do segundo tipo utilizam-se do conteúdo rítmico e tímbrico, este utiliza-se apenas de conceitos da teoria da informação e da geometria de fractais. Entropia, lacunaridade e dimensão do fractal são os parâmetros que treinam os classificadores. Os testes foram realizados com duas coleções criadas para este trabalho e os resultados foram proeminentes / The goal of automatic music genre classification is givingmusic listeners ease and confort when managing digital music databases. Some systems are based on tags of metadata (such as artist name, genre labeled, etc.), while others explore characteristics from the music files to complete the task. While the majority of works of the second type analyse rhytmic, timbric and pitch content, this one explores only information theoretic and fractal geometry concepts. Entropy, fractal dimension and lacunarity are the parameters adopted to train the classifiers. Tests were carried out on two databases assembled by the author. Results were prominent
14

Identificação de padrões de sinais acústicos com base em classificação paraconsistente / Identification of acoustic signal patterns based on paraconsistent classification

Katia Cristina Silva Paulo 20 September 2016 (has links)
Com o uso de um conceito ainda não explorado para fins de classificação de dados, baseado em Lógica Paraconsistente Anotada (LPA), este trabalho visa à construção de um sistema inteligente para classificação de gêneros musicais (Music Genre Classification - MGC). Este tema, de caráter emergente na literatura, tem recebido atenção crescente da comunidade científica, tendo em vista a sua grande aplicabilidade, destacando-se o potencial de comercialização de dados multimídia pela Internet, assim como a automatização de inúmeras tarefas de data mining que envolvem sinais musicais. Utilizando uma base de dados composta por amostras de músicas representativas de cada gênero musical, tais como jazz, bolero, bossa nova, forró, salsa e sertanejo, assim como de um classificador discriminativo paraconsistente, uma abordagem supervisionada é proposta para solucionar o problema. O primeiro módulo do sistema realiza a extração de características dos diversos segmentos das músicas com base na análise tempo-frequência associada com as bandas críticas do ouvido humano. Por outro lado, o segundo módulo utiliza o classificador proposto, que deve permitir a manipulação de sinais com características contraditórias de uma maneira mais semelhante àquela realizada pelo cérebro humano. Os resultados, quando comparados com as abordagens pré-existentes para MGC, demonstram a viabilidade do uso da LPA para tal fim. Além disso, caracteriza-se neste trabalho, uma contribuição original ao estado-da-arte no tema, que consiste justamente no uso da LPA para MGC, procedimento para o qual inexiste descrição na literatura até este momento. / By using a new concept, which is based on Paraconsistent Logic (LPA) and has not yet been applied for classification, this work aims at constructing an intelligent system for Music Genre Classification (MGC). This topic, that is emergent in the literature, has received an increasing attention from the scientific community due to its applicability, emphazising both a commercial potential to commercialize multimedia content on the Internet and data mining tasks involving music signals. By adopting a database formed by samples of songs, which represent different styles of music, such as jazz, bolero, bossa nova, forró, salsa and sertanejo, and a discriminative paraconsistent classifier, a supervised procedure is used to solve the problem. The system is divided in two modules. The first extracts features from the music files, based on the concepts of time-frequency analysis and crictical bands of the human ear. On the other hand, the second implements the proposed classifier, which allows an efficient treatment of contradictions in such a way that is more similar to the human brain. The results obtained, when compared with existing approaches used to MGC, demonstrate how LPA is suitable for this purpose. Additionally, this is the original contribution to the state-of-the-art: the use of LPA for MGC, an inexistent approach up to date.
15

Trilogie Milénium v žánrových proměnách / The Millennium trilogy in the genre transformations

Hofmanová, Petra January 2016 (has links)
In the thesis I would like to focus on the very topical literary work: Millennium trilogy by Stieg Larsson who died in 2004. The trilogy consists of three parts: The girl with the dragon tattoo, The girl who played with fire and The girl who kicked the hornets' nest. The trilogy will be examined from the perspective of genre determination. With the professional literature I would like to describe each genre and it's possibility of use. I would also like to find a typical features of particular genre in different forms: situations, characters, conflicts, etc. One part of my thesis will be dedicated to the writer and his autobiography traits. I would also like to turn to the fourth part of the trilogy by David Lagercrantz. I will try to answer the question why the trilogy has been extended, what literature genre is characteristic for the writer and what changes have been made in content or formal features of the story. During my work I would like to compare the trilogy with other Nordic literature pieces to find the cause of increasing sale of this kind of literature nowadays.
16

A comparative analysis of CNN and LSTM for music genre classification / En jämförande analys av CNN och LSTM för klassificering av musikgenrer

Gessle, Gabriel, Åkesson, Simon January 2019 (has links)
The music industry has seen a great influx of new channels to browse and distribute music. This does not come without drawbacks. As the data rapidly increases, manual curation becomes a much more difficult task. Audio files have a plethora of features that could be used to make parts of this process a lot easier. It is possible to extract these features, but the best way to handle these for different tasks is not always known. This thesis compares the two deep learning models, convolutional neural network (CNN) and long short-term memory (LSTM), for music genre classification when trained using mel-frequency cepstral coefficients (MFCCs) in hopes of making audio data as useful as possible for future usage. These models were tested on two different datasets, GTZAN and FMA, and the results show that the CNN had a 56.0% and 50.5% prediction accuracy, respectively. This outperformed the LSTM model that instead achieved a 42.0% and 33.5% prediction accuracy. / Musikindustrin har sett en stor ökning i antalet sätt att hitta och distribuera musik. Det kommer däremot med sina nackdelar, då mängden data ökar fort så blir det svårare att hantera den på ett bra sätt. Ljudfiler har mängder av information man kan extrahera och därmed göra den här processen enklare. Det är möjligt att använda sig av de olika typer av information som finns i filen, men bästa sättet att hantera dessa är inte alltid känt. Den här rapporten jämför två olika djupinlärningsmetoder, convolutional neural network (CNN) och long short-term memory (LSTM), tränade med mel-frequency cepstral coefficients (MFCCs) för klassificering av musikgenre i hopp om att göra ljuddata lättare att hantera inför framtida användning. Modellerna testades på två olika dataset, GTZAN och FMA, där resultaten visade att CNN:et fick en träffsäkerhet på 56.0% och 50.5% tränat på respektive dataset. Denna utpresterade LSTM modellen som istället uppnådde en träffsäkerhet på 42.0% och 33.5%.
17

Efficient Music Thumbnailing for Genre Classification / Effektiv urvalsteknik för musikgenreklassificering

Skärbo Jonsson, Adam January 2022 (has links)
For music genre classification purposes, the importance of an intelligent and content-based selection of audio samples has been mostly overlooked. One common approach toward representative results is to select samples at predetermined locations. This is done to avoid analysis of the full audio during classification. While methods in music thumbnailing could be used to find representative samples for genre classification, it has not yet been demonstrated. This thesis showed that efficient and genre representative sampling can be performed with a machine learning model (bidirectional RNN with either LSTM or GRU cells). The model was trained using a sub-optimal genre classifier and computationally inexpensive audio features. The genre classifier was used to compute losses for evenly spaced samples in 14000 tracks. The losses were then used as targets during training. Root mean square energy and zero-crossing rate were used as features, computed over relatively large time steps and wide intervals. The proposed framework can be used to give better predictions with trained genre classifiers and most likely also train, or retrain, them for higher classification accuracy at a low computational cost. / Vid musikgenreklassificering har betydelsen av ett intelligent och innehållsbaserat urval allt som oftast förbisetts. En ansats till ett representativt resultat görs vanligtvis genom att ett antal kortare utdrag tas vid förutbestämda tidpunkter. Detta görs för att under en klassificering undvika att analysera hela musikverket. Fastän det existerar metoder inom music thumbnailing för att hitta representativa urval har de ännu inte tillämpats inom genreklassificering. I denna uppsats visades att ett effektivt och genrerepresentativt musikurval kan utföras med en maskininlärningsmodell (dubbelriktad RNN med antingen LSTM- eller GRU-celler). Modellen tränades med hjälp av en suboptimal genreklassificerare och beräkningsmässigt enkla ljudattribut. Genreklassificeraren användes för att beräkna förlusten av jämnt fördelade urval i 14000 musikverk. Förlusterna användes sedan som utdata under träningen. Kvadratiskt energimedelvärde och zero-crossing rate beräknades över relativt långa tidssteg och breda intervall och användes som indata. Det föreslagna ramverket kan till beräkningsmässigt låga kostnader användas för att ge bättre förutsägelser med redan tränade genreklassificerare och sannolikt träna, eller omträna, dessa för högre noggrannhet vid klassificering.
18

Structuration de contenus audio-visuel pour le résumé automatique / Audio-visual content structuring for automatic summarization

Rouvier, Mickaël 05 December 2011 (has links)
Ces dernières années, avec l’apparition des sites tels que Youtube, Dailymotion ou encore Blip TV, le nombre de vidéos disponibles sur Internet aconsidérablement augmenté. Le volume des collections et leur absence de structure limite l’accès par le contenu à ces données. Le résumé automatique est un moyen de produire des synthèses qui extraient l’essentiel des contenus et les présentent de façon aussi concise que possible. Dans ce travail, nous nous intéressons aux méthodes de résumé vidéo par extraction, basées sur l’analyse du canal audio. Nous traitons les différents verrous scientifiques liés à cet objectif : l’extraction des contenus, la structuration des documents, la définition et l’estimation des fonctions d’intérêts et des algorithmes de composition des résumés. Sur chacun de ces aspects, nous faisons des propositions concrètes qui sont évaluées. Sur l’extraction des contenus, nous présentons une méthode rapide de détection de termes. La principale originalité de cette méthode est qu’elle repose sur la construction d’un détecteur en fonction des termes cherchés. Nous montrons que cette stratégie d’auto-organisation du détecteur améliore la robustesse du système, qui dépasse sensiblement celle de l’approche classique basée sur la transcription automatique de la parole.Nous présentons ensuite une méthode de filtrage qui repose sur les modèles à mixtures de Gaussiennes et l’analyse factorielle telle qu’elle a été utilisée récemment en identification du locuteur. L’originalité de notre contribution tient à l’utilisation des décompositions par analyse factorielle pour l’estimation supervisée de filtres opérants dans le domaine cepstral.Nous abordons ensuite les questions de structuration de collections de vidéos. Nous montrons que l’utilisation de différents niveaux de représentation et de différentes sources d’informations permet de caractériser le style éditorial d’une vidéo en se basant principalement sur l’analyse de la source audio, alors que la plupart des travaux précédents suggéraient que l’essentiel de l’information relative au genre était contenue dans l’image. Une autre contribution concerne l’identification du type de discours ; nous proposons des modèles bas niveaux pour la détection de la parole spontanée qui améliorent sensiblement l’état de l’art sur ce type d’approches.Le troisième axe de ce travail concerne le résumé lui-même. Dans le cadre du résumé automatique vidéo, nous essayons, dans un premier temps, de définir ce qu’est une vue synthétique. S’agit-il de ce qui le caractérise globalement ou de ce qu’un utilisateur en retiendra (par exemple un moment émouvant, drôle....) ? Cette question est discutée et nous faisons des propositions concrètes pour la définition de fonctions d’intérêts correspondants à 3 différents critères : la saillance, l’expressivité et la significativité. Nous proposons ensuite un algorithme de recherche du résumé d’intérêt maximal qui dérive de celui introduit dans des travaux précédents, basé sur la programmation linéaire en nombres entiers. / These last years, with the advent of sites such as Youtube, Dailymotion or Blip TV, the number of videos available on the Internet has increased considerably. The size and their lack of structure of these collections limit access to the contents. Sum- marization is one way to produce snippets that extract the essential content and present it as concisely as possible.In this work, we focus on extraction methods for video summary, based on au- dio analysis. We treat various scientific problems related to this objective : content extraction, document structuring, definition and estimation of objective function and algorithm extraction.On each of these aspects, we make concrete proposals that are evaluated.On content extraction, we present a fast spoken-term detection. The main no- velty of this approach is that it relies on the construction of a detector based on search terms. We show that this strategy of self-organization of the detector im- proves system robustness, which significantly exceeds the classical approach based on automatic speech recogntion.We then present an acoustic filtering method for automatic speech recognition based on Gaussian mixture models and factor analysis as it was used recently in speaker identification. The originality of our contribution is the use of decomposi- tion by factor analysis for estimating supervised filters in the cepstral domain.We then discuss the issues of structuring video collections. We show that the use of different levels of representation and different sources of information in or- der to characterize the editorial style of a video is principaly based on audio analy- sis, whereas most previous works suggested that the bulk of information on gender was contained in the image. Another contribution concerns the type of discourse identification ; we propose low-level models for detecting spontaneous speech that significantly improve the state of the art for this kind of approaches.The third focus of this work concerns the summary itself. As part of video summarization, we first try, to define what a synthetic view is. Is that what cha- racterizes the whole document, or what a user would remember (by example an emotional or funny moment) ? This issue is discussed and we make some concrete proposals for the definition of objective functions corresponding to three different criteria : salience, expressiveness and significance. We then propose an algorithm for finding the sum of the maximum interest that derives from the one introduced in previous works, based on integer linear programming.
19

Generování herního prostředí na základě hudby / Game Environment from Music

Vaněk, Jiří January 2009 (has links)
The topic of this work is game environment generation from music. The main subject of this work is the music analysis. I am dealing with the problem of finding relevant information in music, which would be useful for game generating. The design of the system for music analysis, presented in this work, is based on the theory of signal processing and statistical classification. The proposed analysis of music is focused mainly on a beat detection, musical genre recognition and song segmentation. The second part of the work deals with the design of a game, which generates its environment from the data obtained in the music analysis. I have implemented the complete system for the music analysis and a prototype of the game, in which it is possible to evaluate the results from the analysis. The implementation and the achieved results are described in the conclusion of this work.
20

Defining a new Game Genre : Ontological approach to identify and define a new genre of games

Larsson, Andreas January 2023 (has links)
This thesis investigates the genre classification of Vampire Survivors-like games, focusing on "Vampire Survivors" by Poncle (2021, poncle). The objective is to define their genre and explore the possibility of a new genre creation. The research comprehensively examines genre origins, classification approaches, and significance from design, marketing, and consumer perspectives. Vampire Survivors and similar games have gained prominence, but their genre remains uncertain. Elements align with Action Roguelike and Bullet Hell, yet definitive classification proves elusive. This study analyzes gameplay mechanics, design elements, and experiences to compare with established genres.The research provides insights for game developers seeking design patterns and helps players find suitable games. It contributes to the understanding of emerging game genres, promoting innovation in the gaming industry. Using a systematic methodology, this thesis establishes a coherent genre framework. Finding scontribute to genre discussions and inspire future research in this evolving field.

Page generated in 0.7216 seconds