Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2109.10561 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909337543245824 |
|---|---|
| author | Sobhdel, Amirreza Razavi-Far, Roozbeh Palade, Vasile |
| author_facet | Sobhdel, Amirreza Razavi-Far, Roozbeh Palade, Vasile |
| contents | In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimation (SSDE). In previous research on deep supervised SSDE, low accuracies have often resulted from the mismatch between the training data (from known environments) and the test data (from unknown environments). By performing comparative experiments on a sufficient amount of data, we show that the few-shot relation network outperforms other competitors including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and MultiLayer Perceptron (MLP). Hence it is possible to calibrate a microphone-equipped system, with a few labeled samples of audio recorded in a particular unknown environment to adjust and generalize our classifier to the possible input data and gain higher accuracies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_10561 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Few-Shot Learning Approach for Sound Source Distance Estimation Using Relation Networks Sobhdel, Amirreza Razavi-Far, Roozbeh Palade, Vasile Sound Audio and Speech Processing In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimation (SSDE). In previous research on deep supervised SSDE, low accuracies have often resulted from the mismatch between the training data (from known environments) and the test data (from unknown environments). By performing comparative experiments on a sufficient amount of data, we show that the few-shot relation network outperforms other competitors including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and MultiLayer Perceptron (MLP). Hence it is possible to calibrate a microphone-equipped system, with a few labeled samples of audio recorded in a particular unknown environment to adjust and generalize our classifier to the possible input data and gain higher accuracies. |
| title | A Few-Shot Learning Approach for Sound Source Distance Estimation Using Relation Networks |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2109.10561 |