Automatic recognition of bird individuals on an open set using as-is recordings. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Ptacek, Ladislav; Machlica, Lukas; Linhart, Pavel; Jaska, Pavel; Muller, Ludek
• Is Part Of: Bioacoustics. Volume 25:Issue 1(2016:Jan.)
• Page Start: 55
• Page End: 73
• Publication Date: 2016-01-02
• Subjects: Bird individual -- automatic identification -- verification -- Gaussian mixture model -- universal background model; Bioacoustics -- Periodicals; Sound production by animals -- Periodicals; Animal sounds -- Periodicals; Sound recordings -- Periodicals; 591.59405
• DOI: 10.1080/09524622.2015.1089524
• Languages: English
• ISSNs: 0952-4622

Abstract

Abstract: The most common method used to determine the identity of an individual bird is the capture-mark-recapture technique. The method has several major disadvantages, e.g. some species are difficult to capture/recapture and the capturing process itself may cause significant stress in animals leading even to injuries of more vulnerable species. Some studies introduce systems based on methods used for human identification. An automatic system for recognition of bird individuals (ASRBI) described in this article is based on a Gaussian mixture model (GMM) and a universal background model (GMM-UBM) method extended by an advanced voice activity detection (VAD) algorithm. It is focused on recognizing the bird individuals on an open set, i.e. any number of unknown birds may appear anytime during the identification process as is common in nature. The introduced ASRBI processes the recordings just as if they were recorded by an ornithologist: with durations from seconds to minutes, containing noise and unwanted sounds, as well as masking of the singer, etc. Thanks to the VAD algorithm, the proposed system is fully automatic, no manual pre-processing of recordings is needed, neither by cutting off the songs nor syllables. The overall achieved identification accuracy is 78.5%, the lowest 60.3% and the highest 95.7%. In total, 90% of all experiments reach at least 70% accuracy. The result suggests the application of the GMM-UBM with VAD is feasible for individual identification on the open set processing real-life recordings. The described method is capable of reducing both the time consumption and human intervention in animal monitoring projects.