KLASIFIKASI SUARA MANUSIA KE DALAM SOPRAN, MEZZO SOPRAN, ALTO, TENOR, BARITON, BASS DENGAN SELF ORGANIZING MAP

Self Organizing Map adalah metode jaringan syaraf tiruan (Artificial Neural Network) yang biasa digunakan untuk melakukan proses klasifikasi dengan sifat unsupervised learning atau pelatihan tak terbimbing. Cluster yang akan digunakan akan ditentukan secara manual hanya saja dalam prosesnya data yang masuk akan dikelompokan secara otomatis tanpa adanya intevensi dari sistem. Penelitian ini menerapkan Self Organizing Map untuk melakukan klasifikasi data berupa rekaman suara dengan format file WAV karena merupakan format audio yang belum terkompresi ke dalam sopran, mezzo sopran, alto, tenor, baritone, dan bass. Dalam pengambilan data untuk input melalui proses preemphasis, frame, blocking, dan windowing sebelum dirubah menjadi sinyal diskrit dengan Fast Fourier Transform. Data berupa rata-rata magnitude menjadi input dalam sistem klasifikasi Self Organizing Map. Dalam penelitian ini hasil yang didapat belum sesuai dengan harapan karena data tidak mengelompok dengan baik.Self Organizing Map adalah metode jaringan syaraf tiruan (Artificial Neural Network) yang biasa digunakan untuk melakukan proses klasifikasi dengan sifat unsupervised learning atau pelatihan tak terbimbing. Cluster yang akan digunakan akan ditentukan secara manual hanya saja dalam prosesnya data yang masuk akan dikelompokan secara otomatis tanpa adanya intevensi dari sistem. Penelitian ini menerapkan Self Organizing Map untuk melakukan klasifikasi data berupa rekaman suara dengan format file WAV karena merupakan format audio yang belum terkompresi ke dalam sopran, mezzo sopran, alto, tenor, baritone, dan bass. Dalam pengambilan data untuk input melalui proses preemphasis, frame, blocking, dan windowing sebelum dirubah menjadi sinyal diskrit dengan Fast Fourier Transform. Data berupa rata-rata magnitude menjadi input dalam sistem klasifikasi Self Organizing Map. Dalam penelitian ini hasil yang didapat belum sesuai dengan harapan karena data tidak mengelompok dengan baik.

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The Suicide Attempt in an Artificial Neural Network (Self Organizing Map)

Journal of Neurology and Psychology ◽

10.13188/2332-3469.1000031 ◽

2016 ◽

Vol 4 (2) ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Suicide Attempt ◽

Self Organizing Map ◽

Artificial Neural ◽

Self Organizing

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Optimization of BOW Using Self Organizing Map Artificial Neural Network in Similar Images Retrieval Systems

Pattern Recognition and Image Analysis - Lecture Notes in Computer Science ◽

10.1007/978-3-642-38628-2_39 ◽

2013 ◽

pp. 330-339 ◽

Cited By ~ 1

Author(s):

Zied Elleuch ◽

Kirmene Marzouki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Self Organizing Map ◽

Retrieval Systems ◽

Similar Images ◽

Artificial Neural ◽

Self Organizing

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Smart Detection Of AC Series Arc Fault On Home Voltage Line Based On Fast Fourier Transform And Artificial Neural Network

2019 International Electronics Symposium (IES) ◽

10.1109/elecsym.2019.8901671 ◽

2019 ◽

Cited By ~ 1

Author(s):

Muhammad Alwi Abdulrachman ◽

Eka Prasetyono ◽

Dimas Okky Anggriawan ◽

Anang Tjahjono

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Artificial Neural

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Self-organizing map artificial neural network application in multidimensional soil data analysis

Neural Computing and Applications ◽

10.1007/s00521-010-0425-1 ◽

2010 ◽

Vol 20 (8) ◽

pp. 1295-1303 ◽

Cited By ~ 13

Author(s):

Hasan Merdun

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Analysis ◽

Self Organizing Map ◽

Network Application ◽

Artificial Neural ◽

Self Organizing

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Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

SSRN Electronic Journal ◽

10.2139/ssrn.3991002 ◽

2021 ◽

Author(s):

Amir Mosavi ◽

Majid Safaei-Farouji

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hybrid Model ◽

Self Organizing Map ◽

Artificial Neural ◽

Self Organizing

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Intelligent Automatic V6 and V8 Engine Sound Detection Based on Artificial Neural Network

E3S Web of Conferences ◽

10.1051/e3sconf/201913001035 ◽

2019 ◽

Vol 130 ◽

pp. 01035 ◽

Cited By ~ 1

Author(s):

Wenny Vincent ◽

Astuti Winda ◽

Mahmud Iwan Solihin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Intelligent System ◽

Recorded Sound ◽

Sound Detection ◽

Testing Accuracy ◽

Artificial Neural ◽

Artificial Neural Network Ann

The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively.

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