Methods normally used for acoustic individual identification can only compare a single song type, both within and between individuals, to determine identity, i.e. they are call-dependent. Call-independent identification does not involve direct comparison of a particular song type. It can therefore be carried out regardless of the amount of song sharing between individuals, or changes in an individual’s repertoire over time. This wide applicability radically expands the range of situations in which acoustic individual identification can be used. Text-independent recognition is routinely conducted on human speech and in this paper the same techniques, using mel-frequency cepstral coefficients and multilayer perceptrons, were applied to bird song. Call-independent identification accuracies ranged from 54.3-75.7% in three passerine species. To suit bird song better, we modified the feature extraction methods and neural network architecture, resulting in accuracies of 69.3-97.1%. A comparison of call-dependent and call-independent identification showed little difference in accuracy for two species, while the third species had a lower accuracy for the call-independent identification. Our results demonstrate that individual identification from bird song can occur even when direct comparison of a particular song type is not possible.
individual recognition, passerine birds, Mel-frequency cepstral coefficients, artificial neural network, call-independent identification