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Acoustic Event Classification Using Deep Neural Networks
Oguzhan Gencoglu
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Book/Report
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Master's thesis
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Dive into the research topics of 'Acoustic Event Classification Using Deep Neural Networks'. Together they form a unique fingerprint.
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Keyphrases
Deep Neural Network
100%
Acoustic Event Classification
100%
Classification Accuracy
60%
Classification Task
40%
Classification Performance
40%
Supervised Training
40%
High Performance
20%
Popular
20%
Initialize
20%
Parameter Values
20%
Highly Nonlinear
20%
Neural Network
20%
Pattern Recognition
20%
Unsupervised Training
20%
Function Approximation
20%
Recognition Performance
20%
Hidden Layer
20%
Network Weight
20%
Deep Learning Architectures
20%
Batch Size
20%
Stacked Layer
20%
Deep Network
20%
Adjacent Frames
20%
Nonlinear Relation
20%
Greedy Layer-wise Training
20%
Learning Rate
20%
Audio Information Retrieval
20%
Function Classification
20%
Frame number
20%
KNN Classifier
20%
Hidden Layer number
20%
Computer Science
Classification Accuracy
100%
Deep Neural Network
100%
Classification Performance
66%
Conventional Method
66%
Classification Task
66%
Neural Network
33%
Function Approximation
33%
Recognition Performance
33%
Considerable Amount
33%
Parameter Value
33%
Deep Architecture
33%
Learning Rate
33%
Research Subject
33%
Information Retrieval
33%
Pattern Recognition
33%
Engineering
Classification Accuracy
100%
Deep Neural Network
100%
Conventional Method
66%
Classification Performance
66%
Hidden Layer
66%
Classification Task
66%
Supervised Training
66%
Energetics
33%
Pattern Recognition
33%
Batch Size
33%
Adjacent Frame
33%
Research Subject
33%
Earth and Planetary Sciences
Hidden Markov Model
100%
Information Retrieval
100%