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National Repository of Grey Literature 3 records found  Search took 0.00 seconds. 
Deep learning based sound records analysis
 Kramář, Denis ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with the problem of audio-classification of the chainsaw logging sound in natural environment using mainly convolutional neural networks. First, a theory of grafical representation of audio signal is discussed. Following part is devoted to the machine learning area. In third chapter, some of present works dealing with this problematics are given. Within the practical part, used dataset and tested neural networks are presented. Final resultes are compared by achieved accuracy and by ROC curves. The robustness of the presented solutions was tested by proposed detection program and evaluated using objective criteria.
Detailed record
Vliv deforestace krajiny na využívání původních druhů rostlin v okrese Sen Monorom, Kambodža
 Chalupová, Karolína
The bachelor thesis focuses on the extent of landscape deforestation in Cambodia and its impact on the use of native plant species in the Sen Monorom district. To solve this problem a literature search and field research were used. Field research shows monitoring of the occurence and ways of using the native species of the district. Last but not least, a questionnaire survey was used, which focuses mainly on the objective and subjective perception of deforestation. The results of the work show that the deforestation in Cambodia is still increasing. Many species have succumbed to the onslaught of illegal logging and excessive demand. If there is no change in the system and forestry, it’s possible that Cambodia will lose another part of its biodiversity.
Detailed record
Deep learning based sound records analysis
 Kramář, Denis ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with the problem of audio-classification of the chainsaw logging sound in natural environment using mainly convolutional neural networks. First, a theory of grafical representation of audio signal is discussed. Following part is devoted to the machine learning area. In third chapter, some of present works dealing with this problematics are given. Within the practical part, used dataset and tested neural networks are presented. Final resultes are compared by achieved accuracy and by ROC curves. The robustness of the presented solutions was tested by proposed detection program and evaluated using objective criteria.
Detailed record

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