Urban sound classification using long short-term memory neural network

• Autorzy: Lezhenin Iurii , Bogach Natalia , Pyshkin Evgeny

Identyfikatory

DOI

10.15439/2019F185

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Environmental sound classification has received more attention in recent years. Analysis of environmental sounds is difficult because of its unstructured nature. However, the presence of strong spectro-temporal patterns makes the classification possible. Since LSTM neural networks are efficient at learning temporal dependencies we propose and examine a LSTM model for urban sound classification. The model is trained on magnitude mel-spectrograms extracted from UrbanSound8K dataset audio. The proposed network is evaluated using 5-fold cross-validation and compared with the baseline CNN. It is shown that the LSTM model outperforms a set of existing solutions and is more accurate and confident than the CNN.

Słowa kluczowe

EN

environmental sound classification; long short-term memory; convolutional neural networks; UrbanSound8K; data set

PL

klasyfikacja dźwięków środowiskowych; pamięć krótkotrwała; konwolucyjne sieci neuronowe; zbiór danych; UrbanSound8K

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 57--60
• Opis fizyczny: Bibliogr. 28 poz., wz., tab.

Twórcy

autor

Lezhenin Iurii

• Institute of Computer Science and Technology, Peter the Great St.Petersburg Polytechnic University, St.Petersburg, 195251, Russia

autor

Bogach Natalia

• Institute of Computer Science and Technology, Peter the Great St.Petersburg Polytechnic University, St.Petersburg, 195251, Russia

autor

Pyshkin Evgeny

• Software Engineering Lab, University of Aizu, Aizu-Wakamatsu, 965-8580, Japan

Bibliografia

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Uwagi

1. This work was partially supported by the grant 17K00509 of Japan Society for the Promotion of Science (JSPS).

2. Track 1: Artificial Intelligence and Applications

3. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).