Computer-Assisted Pronunciation Training (CAPT) is becoming more and more popular these days. However, the accuracy of existing CAPT systems is still quite low. Therefore, this diploma thesis focuses on improving existing methods for automatic pronunciation evaluation on the segmental level. The first part describes common techniques for this task. Afterwards, we proposed the system based on two approaches. Finally, performed experiments show significant improvement over the reference system.

Neuronové sítě vycházející z architektury WaveNet a sítě využívající rekurentní vrstvy jsou v současnosti používány jak pro syntézu lidské řeči, tak pro „black box“ modelování systémů pro úpravu akustického signálu – modulační efekty, nelineární zkreslovače apod. Úkolem studenta bude shrnout dosavadní poznatky o možnostech využití neuronových sítí při modelování akustických signálů. Student dále implementuje některý z modelů neuronových sítí v programovacím jazyce Python a využije jej pro natrénování a následnou simulaci libovolného efektu nebo systému pro úpravu akustického signálu. V rámci semestrální práce vypracujte teoretickou část práce, vytvořte zvukovou databázi pro trénování neuronové sítě a implementujte jednu ze struktur sítí pro modelování zvukového signálu. Neuronové sítě jsou v průběhu posledních let používány stále více, a to víceméně přes celé spektrum vědních oborů. Neuronové sítě založené na architektuře WaveNet a sítě využívající rekurentních vrstev se v současné době používají v celé řadě využití, zahrnující například syntézu lidské řeči, nebo napřklad při metodě "black-box" modelování akustických systémů, které upravují zvukový signál (modulačí efekty, nelineární zkreslovače, apod.). Tato akademická práce si dává za cíl poskytnout úvod do problematiky neuronových sítí, vysvětlit základní pojmy a mechanismy této problematiky. Popsat využití neuronových sítí v modelování akustických systémů a využít těchto poznatků k implementaci neuronových sítí za cílem modelování libovolného efektu nebo zařízení pro úpravu zvukového signálu.

This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The results show that the system, with proposed temporal convolutional network architecture, produces comparable results with foreign publications. For example, within the SMC dataset, it proved to be the most successful, on the contrary, in the case of other datasets it was slightly below the accuracy of state-of-the-art systems. In addition,the proposed network retains low computational complexity despite increased number of internal parameters.

Goal of this master thesis is to study harmonization based on knowledge of given melody and to design a system which will meaningfully automate this activity. In the work there is covered basics of music theory needed for this topic and previous other approaches to this problematic. There is also covered machine learning, neural networks and recurrent neural networks. In the end, there is outlined design of the system, how to make it work and how to use it. Three experiments were executed with the system. Harmonization of the melodies were unpleasant though. A possible cause might be relatively small used neural network of the system.

The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.

This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to standard feedforward neural network. Best results are achieved using BLSTM architecture. The recurrent neural network are also trained via CTC objective function on the TIMIT dataset. Best result is again achieved using BLSTM architecture.

Les méthodes à l’état de l’art de la reconnaissance de l’écriture sont fondées sur des réseaux de neurones récurrents (RNN) à cellules LSTM ayant des performances remarquables. Dans cette thèse, nous proposons deux nouveaux principes la vérification lexicale et la génération de cohorte afin d’attaquer les problèmes de la reconnaissance de l’écriture : i) le problème des grands lexiques et des décodages dirigés par le lexique ii) la problématique de combinaison de modèles optiques pour une meilleure reconnaissance iii) la nécessité de constituer de très grands ensembles de données étiquetées dans un contexte d’apprentissage profond. La vérification lexicale est une alternative aux décodages dirigés par le lexique peu étudiée à cause des faibles performances des modèles optiques historiques (HMM). Nous montrons dans cette thèse qu’elle constitue une alternative intéressante aux approches dirigées par le lexique lorsqu’elles s’appuient sur des modèles optiques très performants comme les RNN LSTM. La génération de cohorte permet de générer facilement et rapidement un grand nombre de réseaux récurrents complémentaires en un seul apprentissage. De ces deux techniques nous construisons et proposons un nouveau schéma de cascade pour la reconnaissance de mots isolés, une nouvelle combinaison au niveau ligne LV-ROVER et une nouvelle stratégie d’auto-apprentissage de RNN LSTM pour la reconnaissance de mots isolés. La cascade proposée permet de combiner avec la vérification lexicale des milliers de réseaux et atteint des résultats à l’état de l’art pour les bases Rimes et IAM. LV-ROVER a une complexité réduite par rapport à l’algorithme original ROVER et permet de combiner des centaines de réseaux sans modèle de langage tout en dépassant l’état de l’art pour la reconnaissance de lignes sur le jeu de donnéesRimes. Notre stratégie d’auto-apprentissage permet d’apprendre à partir d’un seul réseau BLSTM et sans paramètres grâce à la cohorte et la vérification lexicale, elle montre d’excellents résultats sur les bases Rimes et IAM. / State-of-the-art methods for handwriting recognition are based on LSTM recurrent neural networks (RNN) which achieve high performance recognition. In this thesis, we propose the lexicon verification and the cohort generation as two new building blocs to tackle the problem of handwriting recognition which are : i) the large vocabulary problem and the use of lexicon driven methods ii) the combination of multiple optical models iii) the need for large labeled dataset for training RNN. The lexicon verification is an alternative to the lexicon driven decoding process and can deal with lexicons of 3 millions words. The cohort generation is a method to get easily and quickly a large number of complementary recurrent neural networks extracted from a single training. From these two new techniques we build and propose a new cascade scheme for isolated word recognition, a new line level combination LV-ROVER and a new self-training strategy to train LSTM RNN for isolated handwritten words recognition. The proposed cascade combines thousands of LSTM RNN with lexicon verification and achieves state-of-the art word recognition performance on the Rimes and IAM datasets. The Lexicon Verified ROVER : LV-ROVER, has a reduce complexity compare to the original ROVER algorithm and combine hundreds of recognizers without language models while achieving state of the art for handwritten line text on the RIMES dataset. Our self-training strategy use both labeled and unlabeled data with the unlabeled data being self-labeled by its own lexicon verified predictions. The strategy enables self-training with a single BLSTM and show excellent results on the Rimes and Iam datasets.

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Goal of this master thesis is to study harmonization based on knowledge of given melody and to design a system which will meaningfully automate this activity. In the work there is covered basics of music theory needed for this topic and previous other approaches to this problematic. There is also covered machine learning, neural networks and recurrent neural networks. In the end, there is outlined design of the system, how to make it work and how to use it. Four experiments were executed with the system. Harmonization of the short melodies were unpleasant. Harmonization of longer melodies were overall more successful though. A possible cause might be relatively small used neural network of the system.

A Bicycle-sharing system (BSS) is a popular service scheme deployed in cities of different sizes around the world. Although docked bike systems are its most popular model used, it still experiences a number of weaknesses that could be optimized by investigating bike sharing network properties and evolution of obtained patterns.Efficiently keeping bicycle-sharing system as balanced as possible is the main problem and thus, predicting or minimizing the manual transportation of bikes across the city is the prime objective in order to save logistic costs for operating companies.The purpose of this thesis is two-fold; Firstly, it is to visualize bike flow using data exploration methods and statistical analysis to better understand mobility characteristics with respect to distance, duration, time of the day, spatial distribution, weather circumstances, and other attributes. Secondly, by obtaining flow visualizations, it is possible to focus on specific directed sub-graphs containing only those pairs of stations whose mutual flow difference is the most asymmetric. By doing so, we are able to use graph mining and machine learning techniques on these unbalanced stations.Identification of spatial structures and their structural change can be captured using Convolutional neural network (CNN) that takes adjacency matrix snapshots of unbalanced sub-graphs. A generated structure from the previous method is then used in the Long short-term memory artificial recurrent neural network (RNN LSTM) in order to find and predict its dynamic patterns.As a result, we are predicting bike flows for each node in the possible future sub-graph configuration, which in turn informs bicycle-sharing system owners in advance to plan accordingly. This combination of methods notifies them which prospective areas they should focus on more and how many bike relocation phases are to be expected. Methods are evaluated using Cross validation (CV), Root mean square error (RMSE) and Mean average error (MAE) metrics. Benefits are identified both for urban city planning and for bike sharing companies by saving time and minimizing their cost. / Lånecykel avser ett system för uthyrning eller utlåning av cyklar. Systemet används främst i större städer och bekostas huvudsakligen genom tecknande av ett abonnemang.Effektivt hålla cykel andelssystem som balanseras som möjligt huvud problemand därmed förutsäga eller minimera manuell transport av cyklar över staden isthe främsta mål för att spara logistikkostnaderna för drift companies.Syftet med denna avhandling är tvåfaldigt.För det första är det att visualisera cykelflödet med hjälp av datautforskningsmetoder och statistisk analys för att bättre förstå rörlighetskarakteristika med avseende på avstånd, varaktighet, tid på dagen, rumsfördelning, väderförhållanden och andra attribut.För det andra är det vid möjliga flödesvisualiseringar möjligt att fokusera på specifika riktade grafer som endast innehåller de par eller stationer vars ömsesidiga flödesskillnad är den mest asymmetriska.Genom att göra det kan vi anvnda grafmining och maskininlärningsteknik på dessa obalanserade stationer, och använda konjunktionsnurala nätverk (CNN) som tar adjacency matrix snapshots eller obalanserade subgrafer.En genererad struktur från den tidigare metoden används i det långa kortvariga minnet artificiella återkommande neurala nätverket (RNN LSTM) för att hitta och förutsäga dess dynamiska mönster.Som ett resultat förutsäger vi cykelflden för varje nod i den eventuella framtida underkonfigurationen, vilket i sin tur informerar cykeldelningsägare om att planera i enlighet med detta.Denna kombination av metoder meddelar dem vilka framtida områden som bör inriktas på mer och hur många cykelflyttningsfaser som kan förväntas.Metoder utvärderas med hjälp av cross validation (CV), Root mean square error (RMSE) och Mean average error (MAE) metrics.Fördelar identifieras både för stadsplanering och för cykeldelningsföretag genom att spara tid och minimera kostnaderna.