Publication result detail

DEEP LEARNING BASED SOUND EVENT RECOGNITION

BAJZÍK, J.

Original Title

DEEP LEARNING BASED SOUND EVENT RECOGNITION

English Title

DEEP LEARNING BASED SOUND EVENT RECOGNITION

Type

Paper in proceedings (conference paper)

Original Abstract

The main paper deals with the analysis of the methods of processing and recognition of events in the audio signal and the implementation of the selected method in real use. Recognized events are gunshots placed in a background sound such as traffic noise, human voice, animal sounds and other forms of environmental sounds. For events classification and class recognition, the freely available machine learning framework TensorFlow is used.

English abstract

The main paper deals with the analysis of the methods of processing and recognition of events in the audio signal and the implementation of the selected method in real use. Recognized events are gunshots placed in a background sound such as traffic noise, human voice, animal sounds and other forms of environmental sounds. For events classification and class recognition, the freely available machine learning framework TensorFlow is used.

Keywords

Sound recognition, machine learning, neural network, signal processing

Key words in English

Sound recognition, machine learning, neural network, signal processing

Authors

BAJZÍK, J.

RIV year

2020

Released

25.04.2019

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5735-5

Book

Proceedings of the 25th Conference STUDENT EEICT 2019

Pages from

1

Pages to

4

Pages count

4

BibTex

@inproceedings{BUT162316,
  author="Jakub {Bajzík}",
  title="DEEP LEARNING BASED SOUND EVENT RECOGNITION",
  booktitle="Proceedings of the 25th Conference STUDENT EEICT 2019",
  year="2019",
  pages="1--4",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5735-5"
}