Through-Wall Remote Human Voice Recognition Using Doppler Radar With Transfer Learning
| Autor: | Daegun Oh, Youngwook Kim, Rohan Khanna |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 010401 analytical chemistry Echo (computing) Doppler radar Pattern recognition 01 natural sciences Convolutional neural network 0104 chemical sciences law.invention symbols.namesake law symbols Spectrogram Artificial intelligence Electrical and Electronic Engineering Radar Transfer of learning business Instrumentation Doppler effect Human voice |
| Zdroj: | IEEE Sensors Journal. 19:4571-4576 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2019.2901271 |
| Popis: | We investigated the feasibility of using Doppler radar to recognize human voices by capturing the micro-Doppler signatures of vibrations from the larynx and mouth. The signatures produced through the vibrations of a human being’s vocal cords generate unique micro-Doppler signatures, depending on the letters pronounced. These can then be used to classify and recognize different words and letters. In this paper, we could successfully capture echo signals using the Doppler radar when a human subject spoke seven musical notes from Do to Ti and alphabet letters from $\boldsymbol {A}$ to $\boldsymbol {Z}$ . Spectrogram analysis was conducted for classification purposes, and the deep convolutional neural networks employed could classify the 26 letters to an accuracy of 94%. To overcome the deficiency of the measured data and improve the classification accuracy, transfer learning was introduced. Using the VGG-16 model, its accuracy was improved up to 97%. Additional experiments were conducted to ascertain the radar’s capability to detect the human voice through a barrier between the human and the radar. In this paper, we demonstrated the possibility of remote voice recognition using Doppler information, with or without a barrier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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