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Perspektivní obvodové struktury pro modulární neuronové sítě
Bohrn, Marek ; Ďuračková, Daniela (oponent) ; Husák, Miroslav (oponent) ; Fujcik, Lukáš (vedoucí práce)
Předkládaná práce se zabývá návrhem nové obvodové struktury pro implementace dopředných neuronových sítí. Navržená struktura využívá nekonvenční uspořádání sběrnic pro propojení dílčích bloků. Přínos navržené struktury spočívá v optimalizaci vytížení výpočetních bloků a zvýšení efektivity přenosu dat mezi nimi. Navržená struktura je flexibilní a vhodná pro implementace širokého spektra topologií dopředných neuronových sítí.
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Audio signal modelling using neural networks
Pešán, Michele ; Ištvánek, Matěj (oponent) ; Miklánek, Štěpán (vedoucí práce)
Neural networks based upon the WaveNet architecture and recurrent neural networks are nowadays used in human speech synthesis and other various tasks such as "black-box" modeling systems for acoustic signals alteration (modulation effects, non-linear distortion units, etc.). This work aims, to sum up existing methods of neural network use in acoustic signal modeling. Next, the student is to implement chosen model of neuron network Python and will train this architecture to perform a simulation of desirable sound effect or acoustic alteration system. The task for this semester is, to sum up existing knowledge concerning neural networks. Training database of sound samples and implementation of a sound modeling neural net is to be created as well. Through recent years, neural networks have been used more and more extensively across many science fields. Neural networks based upon the WaveNet architecture and recurrent neural networks are nowadays used in human speech synthesis and other various tasks such as "black-box" modeling systems for acoustic signals alteration (modulation effects, non-linear distortion units, etc.). This academic work provides a brief introduction to the neural network terminology and common practice, elaborates on several types of neural network types, the main focus on DeepMind's WaveNet. Furthermore describes and compares results of experimental implementation of WaveNet and other types of neural network in audio signal "black-box" modeling tasks.
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Audio signal modelling using neural networks
Pešán, Michele ; Ištvánek, Matěj (oponent) ; Miklánek, Štěpán (vedoucí práce)
Neural networks based upon the WaveNet architecture and recurrent neural networks are nowadays used in human speech synthesis and other various tasks such as "black-box" modeling systems for acoustic signals alteration (modulation effects, non-linear distortion units, etc.). This work aims, to sum up existing methods of neural network use in acoustic signal modeling. Next, the student is to implement chosen model of neuron network Python and will train this architecture to perform a simulation of desirable sound effect or acoustic alteration system. The task for this semester is, to sum up existing knowledge concerning neural networks. Training database of sound samples and implementation of a sound modeling neural net is to be created as well. Through recent years, neural networks have been used more and more extensively across many science fields. Neural networks based upon the WaveNet architecture and recurrent neural networks are nowadays used in human speech synthesis and other various tasks such as "black-box" modeling systems for acoustic signals alteration (modulation effects, non-linear distortion units, etc.). This academic work provides a brief introduction to the neural network terminology and common practice, elaborates on several types of neural network types, the main focus on DeepMind's WaveNet. Furthermore describes and compares results of experimental implementation of WaveNet and other types of neural network in audio signal "black-box" modeling tasks.
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Perspektivní obvodové struktury pro modulární neuronové sítě
Bohrn, Marek ; Ďuračková, Daniela (oponent) ; Husák, Miroslav (oponent) ; Fujcik, Lukáš (vedoucí práce)
Předkládaná práce se zabývá návrhem nové obvodové struktury pro implementace dopředných neuronových sítí. Navržená struktura využívá nekonvenční uspořádání sběrnic pro propojení dílčích bloků. Přínos navržené struktury spočívá v optimalizaci vytížení výpočetních bloků a zvýšení efektivity přenosu dat mezi nimi. Navržená struktura je flexibilní a vhodná pro implementace širokého spektra topologií dopředných neuronových sítí.
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