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Results of this study indicate that in the Ortolan Bunting Emberiza hortulana, syllables of the same shape on sonograms (i.e. homologue syllables) often significantly differ between males in frequency parameters. Typically, homologue syllables of different males in the studied population had a similar bandwidth but shifted minimal and maximal frequencies.(More)
In songbirds, song complexity and song sharing are features of prime importance for territorial defence and mate attraction. These aspects of song may be strongly influenced by changes in social environment caused by habitat fragmentation. We tested the hypothesis that habitat fragmentation induced by human activities influences song complexity and song(More)
Individually specific acoustic signals in birds are used in territorial defence. These signals enable a reduction of energy expenditure due to individual recognition between rivals and the associated threat levels. Mechanisms and acoustic cues used for individual recognition seem to be versatile among birds. However, most studies so far have been conducted(More)
The acoustic signals of birds are commonly used for individual recognition. Calls or songs allow discrimination between parent and offspring, between mates and between territorial neighbours and strangers. In this study, we investigated vocal neighbour–stranger discrimination in a nocturnally calling rail species, the Corncrake, Crex crex. We conducted(More)
This paper presents an approach to song-type classification and speaker identification of Norwegian Ortolan Bunting (Emberiza Hortulana) vocalizations using traditional human speech processing methods. Hidden Markov Models (HMMs) are used for both tasks, with features including Mel-Frequency Cepstral Coefficients (MFCCs), log energy, and delta (velocity)(More)
This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden(More)
Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and(More)
Automatic systems for vocalization classification often require fairly large amounts of data on which to train models. However, animal vocalization data collection and transcription is a difficult and time-consuming task, so that it is expensive to create large data sets. One natural solution to this problem is the use of acoustic adaptation methods. Such(More)
Multichannel fusion strategies are presented for the distributed microphone recognition environment, for the task of song-type recognition in a multichannel songbird dataset. The signals are first fused together based on various heuristics, including their amplitudes, variances, physical distance, or squared distance, before passing the enhanced(More)
Movements of animals at large spatial scales are important in ecology and conservation biology, but current methods for monitoring long-distance movements (e.g. ringing or telemetry) are resource demanding and limit sample sizes. Many birds have individually characteristic calls and songs, and recordings may provide an alternative method for monitoring(More)