Utilizing Machine Learning for Signal Classification and Noise Reduction in Amateur Radio

• URL: http://arxiv.org/abs/2402.17771v1
• Date: Thu, 15 Feb 2024 18:49:05 GMT
• Title: Utilizing Machine Learning for Signal Classification and Noise Reduction in Amateur Radio
• Authors: Jimi Sanchez
• Abstract summary: In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds. We explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments. In this paper, we explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations. We investigate the feasibility and effectiveness of employing supervised and unsupervised learning algorithms to automatically differentiate between desired signals and unwanted interference, as well as to reduce the impact of noise on received transmissions. Experimental results demonstrate the potential of machine learning approaches to enhance the efficiency and robustness of amateur radio communication systems, paving the way for more intelligent and adaptive radio solutions in the amateur radio community.

Related papers

• Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals [9.99212997328053]
  This paper proposes the use of a Vector-Quantized Variational Autoencoder (VQ-VAE) to augment training data. The VQ-VAE model generates high-fidelity synthetic RF signals, increasing the diversity and fidelity of the training dataset. Our experimental results show that incorporating VQ-VAE-generated data significantly improves the classification accuracy of the baseline model.
  arXiv  Detail & Related papers  (2024-10-23T21:17:45Z)
• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach. First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms. Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates. Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• Robust Channel Learning for Large-Scale Radio Speaker Verification [30.332141166518287]
  We present a Channel Robust Speaker Learning (CRSL) framework that enhances the robustness of the current speaker verification pipeline. Our framework introduces an augmentation module that mitigates bandwidth variations in radio speech datasets. We also propose an efficient fine-tuning method that reduces the need for extensive additional training time and large amounts of data.
  arXiv  Detail & Related papers  (2024-06-16T14:17:57Z)
• Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning [2.3076690318595676]
  This paper presents a computationally efficient and distributed speaker diarization framework for networked IoT-style audio devices. A Federated Learning model can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Federated Learning model which depends on cosine similarity of speaker embeddings.
  arXiv  Detail & Related papers  (2024-04-16T18:40:28Z)
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN [63.90647197249949]
  In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. In this paper, we present a cooperative radio map estimation approach enabled by the generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2024-02-05T05:01:28Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection [53.063161380423715]
  Existing detection models have shown remarkable success in discriminating known deepfake audio, but struggle when encountering new attack types. We propose a continual learning approach called Radian Weight Modification (RWM) for audio deepfake detection.
  arXiv  Detail & Related papers  (2023-12-15T09:52:17Z)
• Realtime Spectrum Monitoring via Reinforcement Learning -- A Comparison Between Q-Learning and Heuristic Methods [0.0]
  Two approaches for controlling available receiver resources are compared. The Q-learning algorithm used has a significantly higher detection rate than the approach at the expense of a smaller exploration rate.
  arXiv  Detail & Related papers  (2023-07-11T19:40:02Z)
• Radio-Assisted Human Detection [61.738482870059805]
  We propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods. We extract the radio localization and identifer information from the radio signals to assist the human detection. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate can be improved with the aid of radio information.
  arXiv  Detail & Related papers  (2021-12-16T09:53:41Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder [29.590446724625693]
  We present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals.
  arXiv  Detail & Related papers  (2020-11-16T21:41:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.