Accurate Indoor Home Location Classification through Sound Analysis: The 1D-ILQP Approach

Year 2025, Volume: 4 Issue: 1, 12 - 29, 18.02.2025

Nura Abdullahi , Erhan Akbal , Sengul Dogan , Türker Tuncer , Umut Erman

https://doi.org/10.62520/fujece.1422119

Abstract

Detecting human activities within domestic environments constitutes a fundamental challenge in machine learning. Conventionally, sensors and video cameras served as primary tools for human activity detection. However, our work is oriented towards the innovative objective of ascertaining home locations by analyzing environmental sound signals. Consequently, we compiled a comprehensive sound dataset from eight distinct locations. To enable automatic home location detection using this sound dataset, we employed a lightweight machine learning model designed with a paramount focus on precision and minimal computational overhead. At the core of our approach is the introduction of a local feature generator, referred to as the one-dimensional Improved Local Quadruple Pattern (1D-ILQP). This novel 1D-ILQP plays a central role in the feature extraction process, generating textural features from the acoustic signals. To facilitate the extraction of high-level textural features, we emulated the convolutional neural network (CNN) architecture, applying maximum pooling to decompose signals. The suggested 1D-ILQP extracts textural features from each decomposed frequency band as well as the original signal. Subsequently, we selected the top 100 features using the Neighborhood Component Analysis (NCA) technique. The final step of our model involves classification, wherein we employed a range of classifiers, including decision trees, linear discriminant analysis, quadratic discriminant analysis, Naive Bayes, support vector machines, k-nearest neighbor, bagged trees, and artificial neural networks. We subjected the results to a comprehensive evaluation, and all classifiers achieved classification accuracies exceeding 80%. Notably, the k-nearest neighbor classifier delivered the highest classification accuracy, reaching an impressive 99.75%. Our findings unequivocally demonstrate that the proposed sound classification model, based on the 1D-ILQP, has yielded highly satisfactory results when applied to the home location sound dataset.

Keywords

Home location detection, 1D-ILQP, Neighborhood component analysis, Sound classification, Machine learning

Ethical Statement

There is no need to obtain ethics committee permission for the prepared article. There is no conflict of interest with any person/institution in the prepared article.

Ses Analizi Yoluyla Doğru Ev İçi Konumu Sınıflandırması: 1D-ILQP Yaklaşımı

Abstract

Ev ortamlarındaki insan faaliyetlerinin tespit edilmesi, makine öğrenimi alanında temel bir zorluk teşkil etmektedir. Geleneksel olarak sensörler ve video kameralar, insan faaliyetinin tespitinde birincil araçlar olarak hizmet vermiştir. Ancak çalışmamız, çevresel ses sinyallerinin analizi yoluyla ev içi konumlarını belirlemeye yönelik yenilikçi hedefe sahiptir. Sonuç olarak, sekiz farklı lokasyondan gelen verileri kapsayan kapsamlı bir ses veri seti toplanmıştır. Bu ses veri kümesini kullanarak otomatik ev konumu algılamayı etkinleştirmek için, hassasiyete ve minimum hesaplama yüküne odaklanarak hafif bir makine öğrenimi modeli kullanılmıştır. Yaklaşımımızın temelinde, tek boyutlu Geliştirilmiş Yerel Dörtlü Model (GYDM) olarak adlandırılan yerel bir özellik oluşturucunun tanıtılması yer almaktadır. Bu yöntem, akustik sinyallerden dokusal özellikler üreterek özellik çıkarma sürecinde merkezi bir rol oynar. Yüksek seviyeli dokusal özelliklerin çıkarılmasını kolaylaştırmak için, sinyalleri ayrıştırmak için maksimum havuzlama uygulayarak evrişimli sinir ağı mimarisini taklit edilmiştir. Önerilen GYDM, orijinal sinyalin yanı sıra her ayrıştırılmış frekans bandından dokusal özelliklerini çıkarmaktadır. Daha sonra Komşu Bileşen Analizi tekniğini kullanarak en iyi 100 özellik seçilmiştir.Modelimizin son adımı sınıflandırmayı içermektedir. Bu aşamada karar ağaçları, doğrusal diskriminant analizi, ikinci dereceden diskriminant analizi, Naive Bayes, destek vektör makineleri, k-en yakın komşu, torbalanmış ağaçlar ve yapay sinir ağları dahil olmak üzere bir dizi sınıflandırıcı kullanılmıştır. Sonuçlar kapsamlı bir değerlendirmeye tabi tutulmuş ve tüm sınıflandırıcılar %80'in üzerinde sınıflandırma doğruluğuna ulaşmıştır. Özellikle k-en yakın komşu sınıflandırıcı, %99,75 gibi etkileyici bir değere ulaşarak en yüksek sınıflandırma doğruluğu sağlamıştır. Bulgularımız, GYDM’ye dayanan önerilen ses sınıflandırma modelinin, ev konumu ses veri setine uygulandığında oldukça tatmin edici sonuçlar verdiğini açıkça göstermektedir.

Keywords

Ev konum tespiti, Komşu bileşen analizi, Ses sınıflandırma, Makine öğrenmesi

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