Next Article in Journal
Can Mathematical Models of Body Heat Exchanges Accurately Predict Thermal Stress in Premature Neonates?
Next Article in Special Issue
Unsupervised Learning of Total Variability Embedding for Speaker Verification with Random Digit Strings
Previous Article in Journal
Development of Transmission Systems for Parallel Hybrid Electric Vehicles
Previous Article in Special Issue
A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2019, 9(8), 1539;

Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments

Lab of Intelligent Information Processing, Army Engineering University, Nanjing 210007, China
Authors to whom correspondence should be addressed.
Received: 18 March 2019 / Revised: 10 April 2019 / Accepted: 10 April 2019 / Published: 13 April 2019
(This article belongs to the Special Issue Advanced Biometrics with Deep Learning)
PDF [4092 KB, uploaded 13 April 2019]


Even though audio replay detection has improved in recent years, its performance is known to severely deteriorate with the existence of strong background noises. Given the fact that different frames of an utterance have different impacts on the performance of spoofing detection, this paper introduces attention-based long short-term memory (LSTM) to extract representative frames for spoofing detection in noisy environments. With this attention mechanism, the specific and representative frame-level features will be automatically selected by adjusting their weights in the framework of attention-based LSTM. The experiments, conducted using the ASVspoof 2017 dataset version 2.0, show that the equal error rate (EER) of the proposed approach was about 13% lower than the constant Q cepstral coefficients-Gaussian mixture model (CQCC-GMM) baseline in noisy environments with four different signal-to-noise ratios (SNR). Meanwhile, the proposed algorithm also improved the performance of traditional LSTM on audio replay detection systems in noisy environments. Experiments using bagging with different frame lengths were also conducted to further improve the proposed approach. View Full-Text
Keywords: audio replay attack; noise robustness; attention mechanism; long short-term memory audio replay attack; noise robustness; attention mechanism; long short-term memory

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, J.; Zhang, X.; Sun, M.; Zou, X.; Zheng, C. Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments. Appl. Sci. 2019, 9, 1539.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top