Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder
نویسندگان
چکیده
Abstract Passive localization of underwater targets was a thorny problem in acoustics. For traditional model-driven passive methods, the main challenges are inevitable environmental mismatch and presence interference noise everywhere. In recent years, data-driven machine learning approaches have opened up new possibilities for However, acquisition processing acoustics data more restricted than other scenarios, lack is one most enormous difficulties application to To take full advantage relatively easy accessed unlabeled data, this paper proposes framework acoustic source based on two-step semi-supervised classification model. The first step trained unsupervised mode with whole available dataset (labeled dataset), it consists convolutional autoencoder (CAE) feature extraction self-attention (RA) mechanism picking useful features by applying constraints CAE. second supervised labeled dataset, multilayer perceptron connected an encoder from used perform location task. proposed validated uniform vertical line array SWellEx-96 event S5. Compared model without RA, maintains good performance reduced robust when training test distributed differently, which called “data mismatch.”
منابع مشابه
Semi-Supervised Recursive Autoencoder
In this project, we implement the semi-supervised Recursive Autoencoders (RAE), and achieve the result comparable with result in [1] on the Movie Review Polarity dataset1. We achieve 76.08% accuracy, which is slightly lower than [1] ’s result 76.8%, with less vector length. Experiments show that the model can learn sentiment and build reasonable structure from sentence.We find longer word vecto...
متن کاملSentiment Analysis Using Semi-Supervised Recursive Autoencoder
The aim of this project was to use semi-supervised recursive autoencoder provided by [2] and classify the english phrases from movie reviews into five sentiment classes; very positive, positive, neutral, negative and very negative by softmax regression classifier.
متن کاملVariational Autoencoder for Semi-Supervised Text Classification
Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is verified that the decoder’s capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) ...
متن کاملAcoustic Source Localization Based on Beamforming
Sound sources can be localized and analysed using phased microphone arrays.Beamforming is a method for processing microphone array data to produce images that represent the distribution of the acoustic source strength.It is an imaging technique that applies to continuous or discrete source distribution.A microphone array can be designed to be more sensitive to the sound coming from one or more ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2022
ISSN: ['1687-6180', '1687-6172']
DOI: https://doi.org/10.1186/s13634-022-00941-9