There are around 10,426 bird species around the world. Recognizing the bird species for an untrained person is almost impossible either by watching or listening them. In order to identify the bird species from their sounds, there is a need for an application that can detect the bird species from its sound. Time-frequency domain analysis techniques are used to implement the application. We implemented two time-frequency domain feature extraction methods. In feature extraction, a signature matrix which consist of extracted features is created for bird sound signals. A database of signature matrix is created with bird chirps extracted features. We implemented two feature classification methods. They are auto-correlation feature classification method and reference difference feature classification method. An unknown bird chirp is compared with the database to detect the species name. The main aim of the research is to implement the time-frequency domain feature extraction method, create a signature matrix database, implement two feature classification methods and compare them. At last, bird species were identified in the research and the auto-correlation classification method detects the bird species better than the reference difference classification method.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-13624 |
| Date | January 2016 |
| Creators | Vundavalli, Suveen Kumar, Danthuluri, Sri Rama Srinivasa Varma |
| Publisher | Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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