[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117066-en":3,"doc-seo-117066-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117066,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Automatic Fish Classification by Sound Using Machine Learning Techniques - Thesis","An electronic engineering thesis develops an automatic pipeline to classify fish based on sound recordings using machine learning techniques. The work covers signal processing foundations, framing of the problem, and the current state of related acoustic detection approaches. A complete solution is specified and implemented with filtering, segmentation, feature extraction, spectrogram generation, and model fitting. The study evaluates KNN and SVM classifiers and analyzes spectrogram-based results, presenting conclusions, achievements, and future improvements for practical use.","Automatic Fish Classification by Sound Using Machine Learning Techniques  \nJuan Camilo Bernal Fl´orez  \nJuan Pablo Mora P´aez  \nJuanita Marulanda Arg¨uello  \nA thesis presented for the degree in  \nBachelor of Electronic Engineering Supervisor: Juan Carlos Giraldo Carvajal Co-Supervisor: Rafael Andr´es Herrera Guaitero  \nElectronic Engineering Department of Electronics Pontificia Universidad Javeriana Colombia  \nNovember 27, 2023  \nPrincipal of the University: Luis Fernando M´unera Congote, S.J.  \nDean of the School of engineering: Lope Hugo Barrero Solano  \nChair of the program or director of program: Juli´an David Colorado Monta˜no  \nSupervisor: Juan Carlos Giraldo Carvajal  \nCo-Supervisor: Rafael Andr´es Herrera Guaitero  \nArt´ıculo 23 de la resoluci´on No. 13 de junio de 1946  \n“La universidad no se hace responsable de los conceptos emitidos por sus alumnos en sus proyectos de grado. Solo velar´a porque no se publique nada contrario al dogma y la moral cat´olica y porque los trabajos no contengan ataques o pol´emicas puramente personales. Antes bien, que se vea en ellos el anhelo de buscar la verdad y la justicia”.  \nContents  \n1 Introduction 9  \n1.1 Motivation ........................................ 9  \n1.2 Objectives ......................................... 9  \n1.2.1 General objective ................................. 9  \n1.2.2 Specific objectives ................................ 9  \n1.3 Project scope ....................................... 9  \n1.4 Audience ......................................... 9  \n1.5 Organization of the document .............................. 10  \n2 Theoretical framework 10  \n2.1 Fundamental concepts and principles .......................... 10  \n2.1.1 Signal processing ................................. 10  \n2.1.2 Machine learning algorithms .......................... 11  \n2.2 Problem Statement .................................... 13  \n2.3 State of the art ...................................... 13  \n2.3.1 Fish sound detection ............................... 13  \n2.3.2 Machine Learning algorithms for the detection and classification of acoustic signals and or images .............................. 15  \n2.4 Standards ......................................... 15  \n3 Requirements and specifications 16  \n3.1 Solution proposal and analysis ............................. 16  \n3.2 Solution overview ..................................... 16  \n3.3 Block diagram ...................................... 16  \n3.4 Restrictions ........................................ 17  \n3.5 Requirements ....................................... 17  \n3.6 Specifications ....................................... 17  \n4 Architecture design 18  \n4.1 General design description ................................ 18  \n4.2 Flowchart description .................................. 18  \n4.3 Prototype ......................................... 19  \n4.4 Test protocol ....................................... 20  \n4.5 Selection criteria ..................................... 20  \n4.6 Graphic user interface design .............................. 21  \n5 Implementation 21  \n5.1 Filtering .......................................... 21  \n5.2 Segmentation ....................................... 22  \n5.3 Feature extraction .................................... 25  \n5.4 Model Fitting ....................................... 26  \n5.5 KNN classifier ....................................... 26  \n5.6 SVM classifier ....................................... 26  \n5.7 Spectrogram generation ................................. 28  \n5.8 Graphic user interface .................................. 28  \n5.9 Fish dictionary ...................................... 29  \n5.10 Block integration ..................................... 29  \n6 Results Analysis 30  \n6.1 Spectrogram ....................................... 30  \n6.2 Machine learning algorithms ............................... 32  \n7 Project management 35  \n7.1 Metodology ........................................ 35  \n7.2 Work schedule ","cbCaimxFw591j7C1","https://ap.wps.com/l/cbCaimxFw591j7C1","pdf",5406713,1,49,"English","en",105,"# Introduction\n## Motivation\n## Objectives\n## Project scope\n## Audience\n## Organization of the document\n# Theoretical framework\n## Fundamental concepts and principles\n## Problem Statement\n## State of the art\n## Standards\n# Requirements and specifications\n## Solution proposal and analysis\n## Solution overview\n## Block diagram\n## Restrictions\n## Requirements\n## Specifications\n# Architecture design\n## General design description\n## Flowchart description\n## Prototype\n## Test protocol\n## Selection criteria\n## Graphic user interface design\n# Implementation\n## Filtering\n## Segmentation\n## Feature extraction\n## Model Fitting\n## KNN classifier\n## SVM classifier\n## Spectrogram generation\n## Graphic user interface\n## Fish dictionary\n## Block integration\n# Results Analysis\n## Spectrogram\n## Machine learning algorithms\n# Project management\n## Metodology\n## Work schedule\n## Resources, materials and associated costs\n# Conclusions\n## Achievements\n## Future Improvements\n## Alternative Uses\n## Learned Lessons","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To build an automatic system that classifies fish from sound using machine learning methods, supported by signal processing and spectrogram-based analysis.\"},{\"question\":\"Which machine learning classifiers are implemented and tested?\",\"answer\":\"The solution implements and compares a KNN classifier and an SVM classifier as part of the model fitting stage.\"},{\"question\":\"What are the key steps in the implemented pipeline?\",\"answer\":\"Filtering, segmentation, feature extraction, model fitting, and spectrogram generation are used, together with a graphic user interface and integrated system blocks.\"}]","Automatic Fish Classification by Sound Using Machine Learning Techniques - Thesis | PDF",1785673524,123,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"automatic-fish-classification-by-sound-using-machine-learning-techniques-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automatic-fish-classification-by-sound-using-machine-learning-techniques-thesis/117066/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To build an automatic system that classifies fish from sound using machine learning methods, supported by signal processing and spectrogram-based analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifiers are implemented and tested?",{"text":80,"@type":76},"The solution implements and compares a KNN classifier and an SVM classifier as part of the model fitting stage.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key steps in the implemented pipeline?",{"text":84,"@type":76},"Filtering, segmentation, feature extraction, model fitting, and spectrogram generation are used, together with a graphic user interface and integrated system blocks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]