Weakly Supervised Detection of Baby Cry

• URL: http://arxiv.org/abs/2304.10001v3
• Date: Sun, 26 Nov 2023 01:50:59 GMT
• Title: Weakly Supervised Detection of Baby Cry
• Authors: Weijun Tan, Qi Yao, Jingfeng Liu
• Abstract summary: We propose to use weakly supervised anomaly detection to detect a baby cry. In this weak supervision, we only need weak annotation if there is a cry in an audio file.
• Score: 14.778851751964936
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detection of baby cries is an important part of baby monitoring and health care. Almost all existing methods use supervised SVM, CNN, or their varieties. In this work, we propose to use weakly supervised anomaly detection to detect a baby cry. In this weak supervision, we only need weak annotation if there is a cry in an audio file. We design a data mining technique using the pre-trained VGGish feature extractor and an anomaly detection network on long untrimmed audio files. The obtained datasets are used to train a simple CNN feature network for cry/non-cry classification. This CNN is then used as a feature extractor in an anomaly detection framework to achieve better cry detection performance.

Related papers

• Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device [0.0]
  This experiment uses machine learning to classify and recognize a child's voice. If a child is found to be crying or screaming, an alert is immediately sent to the relevant personnel.
  arXiv  Detail & Related papers  (2023-07-27T16:48:19Z)
• Self-supervised learning for infant cry analysis [2.7973623341455602]
  We explore self-supervised learning (SSL) for analyzing a first-of-its-kind database of cry recordings containing clinical indications of more than a thousand newborns. Specifically, we target cry-based detection of neurological injury as well as identification of cry triggers such as pain, hunger, and discomfort. We show that pre-training with SSL contrastive loss (SimCLR) performs significantly better than supervised pre-training for both neuro injury and cry triggers.
  arXiv  Detail & Related papers  (2023-05-02T16:27:18Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning [0.45880283710344055]
  This work presents a fraud and abuse detection framework for streaming services by modeling user streaming behavior. We study the use of semi-supervised as well as supervised approaches for anomaly detection. To the best of our knowledge, this is the first paper to use machine learning methods for fraud and abuse detection in real-world scale streaming services.
  arXiv  Detail & Related papers  (2022-03-04T03:57:58Z)
• A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods [3.198144010381572]
  This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches. Our goal is to help researchers develop more effective video anomaly detection methods.
  arXiv  Detail & Related papers  (2021-11-02T14:00:33Z)
• Audio-visual Representation Learning for Anomaly Events Detection in Crowds [119.72951028190586]
  This paper attempts to exploit multi-modal learning for modeling the audio and visual signals simultaneously. We conduct the experiments on SHADE dataset, a synthetic audio-visual dataset in surveillance scenes. We find introducing audio signals effectively improves the performance of anomaly events detection and outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-28T02:42:48Z)
• Reference-based Defect Detection Network [57.89399576743665]
  The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture. The second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box. We propose a Reference-based Defect Detection Network (RDDN) to tackle these two problems.
  arXiv  Detail & Related papers  (2021-08-10T05:44:23Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization [108.8592577019391]
  Image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. We propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection.
  arXiv  Detail & Related papers  (2020-12-03T10:54:02Z)
• UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance [20.318367304051176]
  We propose a holistic anomaly detection system using deep neural networks for surveillance of critical infrastructures. First, we present a method for the explicit representation of spatial layouts of objects in bird-view images. Then, we propose a deep neural network architecture for unsupervised anomaly detection (UAV-AdNet) Unlike studies in the literature, we combine GPS and image data to predict abnormal observations.
  arXiv  Detail & Related papers  (2020-11-05T14:26:29Z)
• VideoForensicsHQ: Detecting High-quality Manipulated Face Videos [77.60295082172098]
  We show how the performance of forgery detectors depends on the presence of artefacts that the human eye can see. We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
  arXiv  Detail & Related papers  (2020-05-20T21:17:43Z)

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.