A new lateral geniculate nucleus pattern-based environmental sound classification using a new large sound dataset. (July 2022)

• Record Type: Journal Article
• Authors: Taşcı, Burak; Acharya, Madhav R; Datta Barua, Prabal; Metehan Yildiz, Arif; Veysel Gun, Mehmet; Keles, Tugce; Dogan, Sengul; Tuncer, Turker
• Is Part Of: Applied acoustics. Volume 196(2022)
• Publication Date: 2022-07
• Subjects: Environmental sound classification -- Human auditory system -- Human visual system -- Sound classification -- Hand-modeled learning method; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2022.108897
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: A new ESC sound dataset was collected. INCA was used as a feature selection. The LGNPat-MFMDWT-based model reached 93.34% for the classification of 50 commonly used environmental sounds. Abstract: Background and purpose: One of the essential purposes of sound classification is to achieve similar/over classification ability of the human auditory system (HAS). A new dataset and a biologically inspired feature extraction function have been proposed to realize this aim. We have developed a highly accurate sound classification architecture using the proposed biological-inspired feature extraction function. Materials and methods: In this research, a new environmental sound classification (ESC) dataset has been collected as a testbed, and this dataset contains 5000 sounds with 50 classes. Moreover, the collected ESC sound dataset is balanced. A new hand-modeled sound classification model has been proposed to classify sounds of this dataset. This model consists of (i) feature generation using a new lateral geniculate nucleus pattern (LGNPat), statistical moments and discrete wavelet transform (DWT), (ii) loop-based (iterative) neighborhood component analysis (INCA) based feature selection, (iii) classification using the selected features by k nearest neighbors (kNN) with 10-fold cross-validation. The proposed sound classification architecture is an extendable model. In this aspect, a new generation of hand-modeled sound/one-dimensional signal classification methods can be proposed. Results: The presented hand-modeled learning method was applied to the ESC dataset acquired, and our LGNPat-based model attained 93.34% classification. Conclusions: The computed 93.34% on the collected ESC dataset has demonstrated the success of our model. Moreover, the collected dataset has been publicly published. In this aspect, the published dataset can be used to improve advanced sound classification models.