National Repository of Grey Literature 95 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Implementation of the infrastructure for microphone audio streaming into the cloud
Dvořák, Petr ; Smékal, Zdeněk (referee) ; Zvončák, Vojtěch (advisor)
The task of this bachelor's thesis is creation of simple and functional infrastructure designated for recording and transmission of an audio recording captured by microphone and processed by Raspberry Pi. Final output file is then transfered to cloud storage. The goal of the thesis is making a prototype capable of processing a sound recording into required format and with suitable quality. The work should consider possibility of charging the system with battery, type of used network technology and setup of the cloud storage. Prototype should be simple and functional. Importance is comparing the infrastructure with similar devices on the market.
Cyber security of endpoint elements
Fabík, Václav ; Procházka, Rudolf (referee) ; Smékal, Zdeněk (advisor)
This paper describes the design of cyber security for endpoints working on the ARM hardware architecture with the Linux operating system and using the TCP/IP and UDP/IP protocols for network communication. In the introduction, the reader is introduced to the basic concepts of cyber security as well as to the public key infrastructure (PKI) and ways of establishing a secure connection. The reader is introduced to the basic security techniques and the standard regulating security for industrial automation systems. In the practical part, the threat assessment of the presented network infrastructure is elaborated, and security functions that are embedded in NXP i.MX 7 processors and can be used for the cryptographic security of these devices are described.
Modern methods of reconstruction of saturated signals
Beránek, Šimon ; Smékal, Zdeněk (referee) ; Rajmic, Pavel (advisor)
This master's thesis deals with the problems of signal degradation caused by clipping and methods for its removal and signal restoration. Basics of mathematical formulations needed, signal processing and optimalization tasks are described. The goal of this thesis is the implementation of basic algorithms used for hard declipping and creating an audio database later used for testing these algorithms. These implementations are followed by modeling of soft clipping and later restored using the improved algorithms. The restorations are tested using the subjective hearing test MUSHRA and the results are statistically evaluated.
Synthesis of Sound from Video
Lazorčák, Daniel ; Smékal, Zdeněk (referee) ; Říha, Kamil (advisor)
In this thesis, a survey of audio synthesis methods from image and video data to audio data is performed and the implementation of three new synthesis methods is reviewed. The first part of the thesis provides an overview of existing approaches to sound from image, identifying their advantages, limitations and possible extensions. The second part describes the implementation of VSyntha, an application that synthesizes audio from video in real-time with the ability to control musical parameters. The third section describes the ReAmper application, which performs soundscaping using sound objects and musical cues based on the detection and tracking of objects in the image. The fourth section describes the SegMentor application, which creates MIDI files from video using various image segmentation techniques. The implemented methods provide new tools for the creation of audio and multimedia works, open the way for further research and development in the field of sound-from-image synthesis, and provide useful tools for creating audio content and interacting with visual data in the form of audio. The results of this work provide an overview of the current state of research and practice in this area and offer opportunities for further development and applications in practice.
Analysis of prosodic and spectral properties of voice communication in air traffic control
Simonides, Jakub ; Kopřiva, Tomáš (referee) ; Smékal, Zdeněk (advisor)
This thesis analyses the prosodic and spectral features of bi-directional air traffic control communication, describes how to communication was split to segments, according to the source, via transcription. After the splitting, the segments are deeply analyzed for their spectral and prosodic features. The analysis itself, focuses on the spectral aspects of intensity, fundamental frequency F0, slope and centroid. Additionally, tempo and voice activity detection data were measured, to support the spectral aspects as well. Because of the differences between the ATC controller’s and pilots’ spectral aspects, the direction of the communication can be automatically determined, with relatively high success percentage.
The relation of emotions and intonation curves
Gavlasová, Radka ; Smékal, Zdeněk (referee) ; Tučková,, Jana (advisor)
This thesis deals with intonation curves and their relation to human emotions. Besides the theoretical part where you can learn about speech production, signal processing and psychological distribution of emotions, there is also a unique database recorded with the help of two professional actors. The main goal of this thesis is to classify created data using artificial neural networks into four classes. Those classes are anger, joy, boredom and sadness. The practical part was implemented in a programming platform called Matlab using Classification Learner app. Features used for this method were variations of fundamental frequency and MFCC. The results were compared with a listening survey so that it could be determined whether the results provided by neural network are relevant to some kind of a human factor. Success rate of the trained models reached 82 %, new data testing reached 75 %. Listening survey confirmed that the results correspond to the assumption of human perception. Better success rate would be accomplished by using a bigger set of higher quality data.
Detection of Pilot Inattention
Novotný, Josef ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This master thesis deals with the issue of pilot inattention and proposes a design of a system for detecting inattention of general aviation pilots. Inattention belongs to one of the human-caused errors that currently contribute to the most common causes of aviation accidents. The theoretical part deals with the definition of inattention, compares different aviation categories based on flight rules, and contains a search of detection methods. The practical part of the work deals with the selection of suitable sensors, data collection, and implementation of detection algorithms. In this thesis, two different approaches were chosen. The first implementing machine learning using the RUSBoost classifier, which detects states of attention and distraction. The second approach represents the design of a system for detecting pilot inattention based on a set of rules specified in the CLIPS expert system.
Tempo detector based on a neural network
Suchánek, Tomáš ; Smékal, Zdeněk (referee) ; Ištvánek, Matěj (advisor)
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.
Analysis of Expressive Music Performance using Digital Signal Processing Methods
Ištvánek, Matěj ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This diploma thesis deals with methods of the onset and tempo detection in audio signals using specific techniques of digital processing. It analyzes and describes the issue from both the musical and the technical side. First, several implementations using different programming environments are tested. The system with the highest detection accuracy and adjustable parameters is selected, which is then used to test functionality on the reference database. Then, an extension of the algorithm based on the Teager-Kaiser energy operator in the preprocessing stage is created. The difference in accuracy of both systems is compared – the operator has on average increased the accuracy of detection of a global tempo and inter-beat intervals. Finally, a second dataset containing 33 different interpretations of the first movement of Bedřich Smetana’s composition, String Quartet No. 1 in E minor "From My Life". The results show that the average tempo of the entire first movement of the song slightly decreases depending on the later year of the recording.

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