Temat: autoencoders - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Comparison of methods for correcting outliers in ECG-based biometric identification
Autorzy:: Jun, Su
Szmajda, Miroslaw
Khoma, Volodymyr
Khoma, Yuriy
Sabodashko, Dmytro
Kochan, Orest
Wang, Jinfei
Powiązania:: https://bibliotekanauki.pl/articles/221531.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Euclidean distance
autoencoders
outlier correction
ECG signal
human identification
biometrics
Opis:: The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.
Źródło:: Metrology and Measurement Systems; 2020, 27, 3; 387-398
0860-8229
Pojawia się w:: Metrology and Measurement Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

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

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