Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection

• URL: http://arxiv.org/abs/2307.04298v2
• Date: Sat, 26 Aug 2023 14:44:39 GMT
• Title: Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection
• Authors: YeongHyeon Park and Uju Gim and Myung Jin Kim
• Abstract summary: We consider an approach for efficient storage management methods while preserving high-fidelity audio. A computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method.
• Score: 1.4563998247782686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for efficient storage management methods while preserving high-fidelity audio. A hardware-perspective approach, such as using a low-resolution microphone, is an intuitive way to reduce file size but is not recommended because it fundamentally cuts off high-frequency components. On the other hand, a computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended because it also provides a corresponding decoding method. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method. The pre-trained autoencoder is trained for the purpose of audio super-resolution so it can be utilized to encode or decode any arbitrary sampling rate. Moreover, it will reduce the communication cost for data transmission from the edge to the central server. Via the comparative experiments, we confirm that the zero-shot audio compression and decompression highly preserve anomaly detection performance while enhancing storage and transmission efficiency.

Related papers

• FFCA-Net: Stereo Image Compression via Fast Cascade Alignment of Side Information [44.88123177525665]
  Multi-view compression technology, especially Stereo Image Compression (SIC), plays a crucial role in car-mounted cameras and 3D-related applications. We propose a Feature-based Fast Cascade Alignment network (FFCA-Net) to fully leverage the side information on the decoder. Our approach achieves significant gains in terms of 3 to 10-fold faster decoding speed than other methods.
  arXiv  Detail & Related papers  (2023-12-28T11:12:03Z)
• High Fidelity Neural Audio Compression [92.4812002532009]
  We introduce a state-of-the-art real-time, high-fidelity, audio leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary.
  arXiv  Detail & Related papers  (2022-10-24T17:52:02Z)
• Unrolled Compressed Blind-Deconvolution [77.88847247301682]
  sparse multichannel blind deconvolution (S-MBD) arises frequently in many engineering applications such as radar/sonar/ultrasound imaging. We propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time.
  arXiv  Detail & Related papers  (2022-09-28T15:16:58Z)
• Optimizing Image Compression via Joint Learning with Denoising [49.83680496296047]
  High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras. We propose a novel two-branch, weight-sharing architecture with plug-in feature denoisers to allow a simple and effective realization of the goal with little computational cost.
  arXiv  Detail & Related papers  (2022-07-22T04:23:01Z)
• Memory Replay with Data Compression for Continual Learning [80.95444077825852]
  We propose memory replay with data compression to reduce the storage cost of old training samples. We extensively validate this across several benchmarks of class-incremental learning and in a realistic scenario of object detection for autonomous driving.
  arXiv  Detail & Related papers  (2022-02-14T10:26:23Z)
• Audio Defect Detection in Music with Deep Networks [8.680081568962997]
  Deliberate use of artefacts such as clicks in popular music calls for data-centric and context sensitive solutions for detection. We present a convolutional network architecture following end-to-end encoder decoder configuration to develop detectors for two exemplary audio defects.
  arXiv  Detail & Related papers  (2022-02-11T15:56:14Z)
• SoundStream: An End-to-End Neural Audio Codec [78.94923131038682]
  We present SoundStream, a novel neural audio system that can efficiently compress speech, music and general audio. SoundStream relies on a fully convolutional encoder/decoder network and a residual vector quantizer, which are trained jointly end-to-end. We are able to perform joint compression and enhancement either at the encoder or at the decoder side with no additional latency.
  arXiv  Detail & Related papers  (2021-07-07T15:45:42Z)
• Fine granularity access in interactive compression of 360-degree images based on rate-adaptive channel codes [25.563269927604395]
  We propose a new interactive compression scheme for omnidirectional images. We show that our coder obtains a better transmission rate than the state-of-the-art tile-based methods at a small cost in storage.
  arXiv  Detail & Related papers  (2020-06-25T08:13:48Z)
• Content Adaptive and Error Propagation Aware Deep Video Compression [110.31693187153084]
  We propose a content adaptive and error propagation aware video compression system. Our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system.
  arXiv  Detail & Related papers  (2020-03-25T09:04:24Z)

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.