Identification of bird species based on their vocalization is studied in this paper. The main focus is introducing a new parametric representation of bird sounds for automatic identification of their species. The method is based on the statistics of local temporal patterns in bird vocalization. Two different sets of bird species are used in the classification tests. The first set contains six species that often produce inharmonic sounds. For the second set, four species that produce very different types of sounds were added. Recognition results using a k-NN-classifier shows improved recognition accuracy over the results obtained by MFCC-features.

The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.

This paper is related to the development of signal processing techniques for automatic recognition of bird species. Three different parametric representations are compared. The first representation is based on sinusoidal modeling which has been earlier found useful for highly tonal bird sounds. Mel-cepstrum parameters are used since they have been found very useful in the parallel problem of speech recognition. Finally, a vector of various descriptive features is tested because such models are popular in audio classification applications, and bird song is almost like music. We briefly introduce the methods and evaluate their performance in the classification and recognition of both individual syllables and song fragments of 14 common North-European Passerine bird species

The paper concerns the development of a system for the recognition of a context or an environment based on acoustic information only. Our system uses Mel-frequency cepstral coefficients and their derivatives as features, and continuous density hidden Markov models (HMM) as acoustic models. We evaluate different model topologies and training methods for HMMs and show that discriminative training can yield a 10% reduction in error rate compared to maximum-likelihood training. A listening test is made to study the human accuracy in the task and to obtain a baseline for the assessment of the performance of the system. Direct comparison to human performance indicates that the system performs somewhat worse than human subjects do in the recognition of 18 everyday contexts and almost comparably in recognizing six higher level categories.