The aim of this systematic literature review (SLR) is to identify and critically evaluate current
research advancements with respect to small data and the use of data augmentation …

Automatic environmental sound classification (ESC) is one of the upcoming areas of
research as most of the traditional studies are focused on speech and music signals …

Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …

The fast advancement of urban transportation systems in the recent decades has on one
hand improved efficiency in traffic control and management, yet on the other hand brought …

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 …

There has been an increase in the implementation of Artificial Intelligence (AI) in the dairy
industry for Milk Quality Analysis (MQA). However, traditional Machine Learning (ML) …

Birdsongs are highly valuable for bird studies as they provide insights into various aspects
such as species distribution, population structures, and habitat. Recognizing birdsongs …

Acoustic Scene Classification aims to recognize the unique acoustic characteristics of an
environment. Recently, Convolutional Neural Networks (CNNs) have boosted the accuracy …

Animal sound classification (ASC) refers to the automatic identification of animal categories
by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based …

One dimension convolutional neural networks (1D CNN) that directly take raw waveforms as
input has less competition than 2D CNN recognizing environmental sound. In order to …