Dog cough sound classification using artificial neural network and the selected relevant features from discrete wavelet transform

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Kakabutr P., Chen K.S., Wangvisavawit V., Padungweang P., Rojanapornpun O.

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2017

หน้าแรก: 121

หน้าสุดท้าย: 125

จำนวนหน้า: 5

ISBN: 9781467390774

นอก: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017504861&doi=10.1109%2fKST.2017.7886118&partnerID=40&md5=752e8a9a1abe8b18d2735af4881878ff

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

Coughing is one of the important signs of several diseases in dogs. There are two types of dog cough: dry cough and productive cough. The latter is most often associated with an infectious condition. It is difficult to differentiate between the two types even by experienced practitioners. In this paper, an automatic cough sound classification using neural network is introduced. A discrete wavelet transform is employed to decompose the cough sound into low frequency and high frequency components. The statistical features of these components are used as the sound features. The discrimination power of these features in classification are evaluated. Finally, the artificial neural network is used to classify dog cough sound using a subset of discriminant features. The experimental results show that classifying dog cough sounds needs only one fourth of all features and an average accuracy as high as 90% is achievable. ฉ 2017 IEEE.

คำสำคัญ

Dog cough sound