SKLBP14: Kare çekirdekli yerel ikili modele dayalı yeni bir dokusal çevresel ses sınıflandırma modeli

Year 2023, , 46 - 54, 14.06.2023

Arif Metehan Yıldız , Mehmet Veysel Gun Kubra Yıldırım Tugce Keles Sengul Dogan Turker Tuncer U. Rajendra Acharya

https://doi.org/10.5505/fujece.2023.03521

Abstract

Günümüzde ileri-ileri (FF) algoritması, makine öğrenimi toplumunda çok popülerdir ve kare tabanlı bir aktivasyon işlevi
kullanır. Bu araştırmada, FF algoritmasından ilham aldık ve yerel ikili örüntü için yeni bir çekirdek sunduk ve bu, kare çekirdekli
yerel ikili örüntü (SKLBP) olarak adlandırıldı. Önerilen tek boyutlu SKLBP'yi konuşlandırarak, yeni bir özellik mühendisliği
modeli sunulmuştur. Önerilen SKLBP tabanlı modelin sınıflandırma yeteneğini ölçmek için, yeni bir dokusal çevresel ses
sınıflandırması (ESC) veri seti topladık. Toplanan veri seti dengeli bir veri seti olup 15 sınıf içermektedir. Her sınıfta 100 ses
vardır. Önerdiğimiz model derin öğrenme yapısını taklit etmiştir. Bu nedenle, ayrık dalgacık dönüşümü kullanarak çok düzeyli
öznitelik çıkarma metodolojisini kullanır. Oluşturulan özellikler, yinelemeli özellik seçicinin girdisi olarak kabul edilmiştir.
Seçilen öznitelik vektörü k en yakın komşu sınıflandırıcının girdisi olarak kullanılmıştır. Önerilen SKLBP tabanlı sinyal
sınıflandırma modeli, %90'ın üzerinde doğruluğa ulaştı. Bu bağlamda, yeni dokusal ESC veri setini toplayarak ve SKLBP tabanlı
ESC modelini önererek ESC metodolojisine katkıda bulunduk.

Keywords

Textural ESC dataset, Square-kernelled local binary pattern, Signal classification, Advanced signal processing, Textural feature extraction

SKLBP14: A new textural environmental sound classification model based on a squarekernelled local binary pattern

Abstract

Nowadays, the forward-forward (FF) algorithm is very popular in the machine learning society, and it uses a square-based activation
function. In this research, we inspired the FF algorithm and presented a new kernel for a local binary pattern named square-kernelled
local binary pattern (SKLBP). By deploying the proposed one-dimensional SKLBP, a new feature engineering model has been
presented. To measure the classification ability of the proposed SKLBP-based model, we have collected a new textural environmental
sound classification (ESC) dataset. The collected dataset is a balanced dataset, and it contains 15 classes. There are 100 sounds in each
class. Our proposed model has mimicked the deep learning structure. Therefore, it uses multileveled feature extraction methodology
by using discrete wavelet transform. The features generated have been considered as input for the iterative feature selector. The chosen
feature vector has been utilized as input of the k nearest neighbor classifier. The proposed SKLBP-based signal classification model
reached 94% classification accuracy. In this aspect, we contributed to the ESC methodology by collecting the new textural ESC dataset
and proposing the SKLBP-based ESC model.

Keywords

Dokusal ESC veri seti, Kare çekirdekli yerel ikili model, Sinyal sınıflandırması, Gelişmiş sinyal işleme, Dokusal özellik çıkarma

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