Deep Learning based Crime Detection and Resource Creation Approach From Bengali Voice Calls

Tracking #: 735-1715


Responsible editor: 

Bharathi Raja Chakravarthi

Submission Type: 

Research Paper

Abstract: 

Mobile phones have revolutionized our way of communication. Despite its numerous benefits, it has become a great utility for conducting crimes or making threats. Due to the large number of users it is almost impossible for security forces to take proactive measures against those crimes. In this paper, with the help of machine learning, we focus on building a system that can detect potential threats in phone calls. We develop (to the best of our knowledge) the very first Bengali voice call dataset to train the machine learning system. Our system takes a voice call and uses a Deep 1D Convolutional Neural Network to analyze the call and a Multi-Layer Perceptron to decide whether any threats exist or not. The proposed simple baseline solution, trained on our $\sim$9hrs. worth voice call dataset, is able to achieve $91$\% precision, recall and F1-score in detecting the crime calls. We believe, in future these systems will aid in assisting in investigations, evaluating voice conversations, and giving predictions and estimations for potential threats. All of our recorded calls are freely available to use by the future researchers at: https://tinyurl.com/detecThreats

Manuscript: 

Tags: 

  • Reviewed

Data repository URLs: 

Date of Submission: 

Wednesday, November 30, 2022

Date of Decision: 

Friday, March 17, 2023


Nanopublication URLs:

Decision: 

Reject

Solicited Reviews:


2 Comments

Review the paper and comment.

Positive Comments:

-The research paper proposes a unique approach to detect potential threats in phone calls using deep learning. 
-The proposed system uses a Deep 1D Convolutional Neural Network to analyze the calls and a Multi-Layer Perceptron to decide whether any threats exist or not. 
-The proposed simple baseline solution is able to achieve 91% precision, recall and F1-score in detecting the crime calls. 
-The recorded calls are freely available to use by the future researchers. 

Negative Comments:

-The research paper does not provide any specifics on the Multi-Layer Perceptron used in the system. 
-The research paper does not provide any information on the challenges faced while collecting the dataset. 
-The research paper does not provide any information on the potential applications of the system.

Meta-Review by Editor

There are many question from reviewers which are not answered in the paper. I encorage the authors to consider the reviewers suggestion carfully to improve the paper for future work.

Important questions to take care while updating the papers are 

1) Annotation process inculding the guidelines and annotators (reviewer 1 and reviewer 2 question)-- since the papers main contribution is dataset

2) Situation in code-mixed -- real world senario (reviewer 1 question)

3) Lack of analysis (reviewer 2 question)

4) Details about the models used

Bharathi Raja Chakravarthi (https://orcid.org/0000-0002-4575-7934)