Identifying the population of animals through pitch, formant, short time energy-A sound analysis

Raju, N.; Mathini, S.; Priya, T. Lakshmi; Preethi, P.; Chandrasekar, M.

doi:10.1109/icceet.2012.6203766

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

2018

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The domestic cat (Feliscatus) is one of the most attractive pets in the world, and it generates mysterious kinds of sound according to its mood and situation. In this paper, we deal with the automatic classification of cat sounds using machine learning. Machine learning approach for the classification requires class labeled data, so our work starts with building a small dataset named CatSound across 10 categories. Along with the original dataset, we increase the amount of data with various audio data augmentation methods to help our classification task. In this study, we use two types of learned features from deep neural networks; one from a pre-trained convolutional neural net (CNN) on music data by transfer learning and the other from unsupervised convolutional deep belief network that is (CDBN) solely trained on a collected set of cat sounds. In addition to conventional GAP, we propose an effective pooling method called FDAP to explore a number of meaningful features. In FDAP, the frequency dimension is roughly divided and then the average pooling is applied in each division. For the classification, we exploited five different machine learning algorithms and an ensemble of them. We compare the classification performances with respect following factors: the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional GAP or FDAP, and the machine learning algorithms used for the classification. As expected, the proposed FDAP features with larger amount of data increased by augmentation combined with the ensemble approach have produced the best accuracy. Moreover, both learned features from pre-trained CNN and unsupervised CDBN produce good results in the experiment. Therefore, with the combination of all those positive factors, we obtained the best result of 91.13% in accuracy, 0.91 in f1-score, and 0.995 in area under the curve (AUC) score.

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Section: Introductionmentioning

confidence: 99%

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

2018

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“…The pet animal sound classification and recognition using deep learning techniques can be a growing area of research. The data driven approach for acoustic signal got high interest in recent year and some study also focus 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 is studied on [4,5].…”

Section: Introductionmentioning

confidence: 99%

Domestic Cat Sound Classification Using Transfer Learning

Pandeya¹,

Lee²

2018

IJFIS

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The domestic cat or house cats (Felis catus) are an ancient human pet animal that can deliver various alert message to human on environmental changes by its mysterious kinds of sounds generation capability. Cat sound classification using deep neural network had scarcity of labeled data, that impelled us to make CatSound dataset across 10 categories of sound. The dataset was even not enough to select data driven approach for end to end learning, so we choose transfer learning for feature extraction. Extracted feature are input to six various classifiers and ensemble techniques applied with predicted probabilities of all classifier results. The ensemble and data augmentation perform better in this research. Finally, various results are evaluated using confusion matrix and receiver operating characteristic curve.

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“…Short time energy analysis is a general method of analyzing acoustic signal in time domain and commonly employed in monitoring and identifying the acoustic signals [11], such as analyzing the animal population through studying their calls [12], or recognizing the music instrument through analyzing chord music [13], and so on.…”

Section: Principle Of Short Time Energymentioning

confidence: 99%