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The project covers prerequisite setup including Python (3.8+), required libraries and specific hardware/OS requirements, followed by data collection from an open Kaggle audio corpus. It provides environment setup and execution steps to run BabycryAnalysisNB.ipynb, then outlines the code flow: library imports, parameter initialization, audio loading with MFCC feature extraction, exploratory data analysis, data reshaping, model training across traditional, deep, hybrid, and ensemble approaches, and handling class imbalance with SMOTE.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/configuration-manual-configuration-manual-msc-data-analytics-research-project/290878/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/configuration-manual-configuration-manual-msc-data-analytics-research-project/290878.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the objective of the baby cry classification study described in the manual?","Question",{"text":112,"@type":113},"The manual explains a workflow for classifying baby cries using machine learning models ranging from SVM, KNN, and Random Forest to LSTM/RNN and hybrid/ensemble methods.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What software and hardware requirements are specified?",{"text":117,"@type":113},"Python 3.8+ with development environments like PyCharm or Anaconda Navigator is required, along with libraries including numpy, pandas, tensorflow/keras, xgboost, and torch. The system requirements list Windows 11, an Intel i7/i9 CPU, at least 8GB RAM, and 30GB disk space.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the dataset collected and how is imbalance handled?",{"text":121,"@type":113},"The project uses labeled baby-cry audio files from a Kaggle repository and saves the dataset locally. 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All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Kalyani Phursule |\n| --- | --- |\n| Date: | 28th January 2025 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nConfiguration Manual  \nKalyani Phursule  \n23229268  \n1 Introduction 1  \nThis research is about baby cry classification using machine learning models ranging from simple models like, SVM, KNN, RF,etc to advanced models like LSTM, RNN,etc including hybrid and ensemble approach for machine learning. The purpose of this document is to provide details regarding the pre-requisite such as software and hardware tools used in the developing the study, steps for collecting data and executing code.  \n2 Pre-requisite  \nThe software and hardware requirements used are as follows:  \n2.1 Software Requirements  \n2.1.1 Python Environment  \nPython Version: 3 .8 or higher. Pycharm or Anaconda Navigator as development environments.  \n2.1.2 Python Libraries  \n\n| Python Lib | Version |\n| --- | --- |\n| numpy | 1.24.3 |\n| pandas | 2.0.3 |\n| matplotlib | 3.7.5 |\n| seaborn | 0.13.2 |\n| sklearn | 1.3.0 |\n| tensorflow | 2.13.0 |\n| keras | 2.13.1 |\n| xgboost | 2.1.1 |\n| scipy | 1.10.1 |\n| torch | 2.4.1 |\n\nTable 1: Python libraries and their versions used in the project.  \n2.2 Hardware Requirements  \n\n| Requirement | Specification |\n| --- | --- |\n| OS | Windows 11 |\n| CPU | Intel i7/i9 (10 cores or up) |\n| RAM | 8GB or higher |\n| Disk Space | 30 GB |\n\nTable 2: System requirements for running the project.  \n3 Data Collection  \nFor this study open source data from Kaggel 1 data repository was used. It is set of labelled baby cries audio files with classes such as hungry, discomfort, tired, belly pain, and burping.  \nDownload the dataset from the url and save it locally.  \n4 Environment Setup  \n1. Install PyCharm Community or Professional edition from JetBrains.  \n2. Open the PyCharm and set interpreter as 3 .8  \nGoto –> File –> Setting –> Python interpreter –> Select version 3 .8 or above  \n5 Steps to execute code  \nThis section gives all required steps to execute the code and reproduce the results.  \n1 data url: [https://www.kaggle.com/datasets/warcoder/infant-cry-audio-corpus](https://www.kaggle.com/datasets/warcoder/infant-cry-audio-co","cbCait8DvMKiDvGk","https://ap.wps.com/l/cbCait8DvMKiDvGk","pdf",2275052,11,"English","# 1 Introduction\n# 2 Pre-requisite\n## 2.1 Software Requirements\n## 2.2 Hardware Requirements\n# 3 Data Collection\n# 4 Environment Setup\n# 5 Steps to execute code\n# 6 Flow of the Code\n## 6.1 Importing required libraries\n## 6.4 Data Loading and MFCC feature extraction\n## 6.7 Model Training\n## 6.8 Imbalance Data Handling\n## 6.10 Model Evaluation","[{\"question\":\"What is the objective of the baby cry classification study described in the manual?\",\"answer\":\"The manual explains a workflow for classifying baby cries using machine learning models ranging from SVM, KNN, and Random Forest to LSTM/RNN and hybrid/ensemble methods.\"},{\"question\":\"What software and hardware requirements are specified?\",\"answer\":\"Python 3.8+ with development environments like PyCharm or Anaconda Navigator is required, along with libraries including numpy, pandas, tensorflow/keras, xgboost, and torch. The system requirements list Windows 11, an Intel i7/i9 CPU, at least 8GB RAM, and 30GB disk space.\"},{\"question\":\"How is the dataset collected and how is imbalance handled?\",\"answer\":\"The project uses labeled baby-cry audio files from a Kaggle repository and saves the dataset locally. Data imbalance is addressed by applying SMOTE and retraining the proposed models with balanced data.\"}]","Configuration Manual - Configuration Manual - MSc Data Analytics Research Project | PDF",28]