- Tytuł:
- Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition
- Autorzy:
-
Kleć, M.
Korzinek, D. - Powiązania:
- https://bibliotekanauki.pl/articles/952940.pdf
- Data publikacji:
- 2015
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
Sparse Autoencoders
deep learning
genre recognition
Scattering Wavelet Transform - Opis:
- Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scatter- Ing Wavelet Transform (SWT) for classifying musical genres. The SWT uses A sequence of Wavelet Transforms to compute the modulation spectrum coef- Ficients of multiple orders, which has already shown to be promising for this Task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is Used to boost the well-known GTZAN database, which is a standard bench- mark for this task. The final classifier is tested using a 10-fold cross validation To achieve results similar to other state-of-the-art approaches.
- Źródło:
-
Computer Science; 2015, 16 (2); 133-144
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł