## 1 Introduction  
The sound generation and perception systems of animals have evolved to help them to survive in their environment. From an evolutionary perspective, the intentional sounds that are generated by animals should be distinct from the random sounds of the environment. Some animals have special sensory capabilities, such as vision, sights, feeling, and awareness of natural changes as compared to human beings. Animal sounds can be helpful for human beings in terms of security, prediction of natural disasters, and intimate interactions if we are able to recognize them properly. The data-driven machine learning approach for acoustic signal has been of great interest to researchers in recent years, and some studies have been conducted on animal sound classification [1; 2] and animal population identification based on their sound characteristics [3]. The marine mammal sound classification and its impact on marine life have been studied in [4; 5]. Several studies [6; 7; 8; 9; 10; 11; 12] have focused on bird sound identification, classification, and its challenges. The study in [13] has performed the insect species classification based on their sound signals. The prediction of unusual animal sound behavior during the earthquake and natural disaster has been studied using machine learning techniques in [14]. Recently, the possibility of transfer learning from music features for bird sound identification using hidden Markov models (HMMs) has been examined in [15]. The pet animals are close friends of the human from the time of human evolution, and they deliver their messages by producing some identical sounds. Most pets spend their whole time in human peripheries, thus sound analysis of pet animals is important. Domestic cat is one of the most widely loved pet animals in the world, and the whole population is around 88.3 million, according to the report of Live Science (2013). The behavioral analysis of domestic cat is explained well in [16; 17]. The attempt for environmental sound recognition using HMMs [18] shows classification of 15-classes of various environmental sounds. Among 15-classes also include animal sounds (bird call, dog barking, and cat meowing) and shows better sound recognition accuracy using universal modeling based on GMM clustering rather than simple HMM and balanced universal modeling. The domestic cat sound classification using transfer learning [19] is a primary step to classify cat sounds with limited data. The classification of cat sounds is an attempt to communicate better with domestic cat, and hence possibly to understand their intentions well. This paper is an extension of [19] that particularly focuses on dealing with small datasets for automatic classification of domestic cat sounds using machine learning algorithms. The data driven approaches requires class labeled data, therefore our work starts with building a small set of cat sound dataset. Our goal is to achieve a general purpose classification system for domestic cat that is independent from the issues, such as the bio-diversity, species variation, and age variation, so we try to include many kinds of cat sounds in different moods. Because the set of collected data is not enough, we increase the amount of data using various audio augmentation methods. As in [20], audio augmentation is performed by random selection of augmentation methods within the appropriate ranges of parameter values, as described more in Section 3.1. As there is little knowledge about which audio features are efficient for our purpose, instead of hand-crafted features, we use two kinds of learned features from deep neural networks: convolutional neural network (CNN) and convolutional deep belief network (CDBN). The CNN features are obtained from pre-trained network on music data by transfer learning. Meanwhile, the unsupervised CDBN network features are extracted from the network solely trained on a collected set of cat sounds without class labels.