this paper is on procedures whereby new (e.g., juvenile) members of a population could learn to communicate with the other members by observing their communicative behavior. Two apparently distinct issues are relevant to the evaluation of such learning procedures. First, the procedure must enable the new members to accurately acquire the communication system of the population, even though their observations may be limited, noisy, or otherwise misleading. Second, the learning procedure used by its new members will affect the population's communication system over time. The use of a particular procedure might result in the population's communication increasing in coordination, ultimately yielding a nearly optimally coordinated system. If a learning procedure were to satisfy both criteria, it could explain how learned communication systems are maintained over time, as well as how they are established in the first place.

Communication signals in many animal species (including humans) show a surprising amount of variety both across time and at any one instant in a population. Traditional accounts and simulation models of the evolution of communication offer little explanation of this diversity. Sexual selection of signals used to attract mates, and the coevolving preferences used to judge those signals, can instead provide a convincing mechanism. Here we demonstrate that a wide variety of "songs" can evolve when male organisms sing their songs to females who judge each male's output and decide whether or not to mate with him based on their own coevolved aesthetics. Evolved variety and rate of innovation are greatest when females combine inherited song preferences with a desire to be surprised. If females choose mates from a small pool of candidates, diversity and rate of change are also increased. Such diversity of communication signals may have implications for the evolution of brai...

Human language is unique among the communication systems of the natural world. The vocabulary of human language is unique in being both culturally-transmitted and symbolic. In this paper I present an investigation into the factors involved in the evolution of such vocabulary systems. I investigate both the cultural evolution of vocabulary systems and the biological evolution of learning rules for vocabulary acquisition.

Syllables are elementary building blocks of bird song. In sounds of many songbirds a large class of syllables can be approximated as amplitude and frequency varying brief sinusoidal pulses. In this article we test how well bird species can be recognized by comparing simple sinusoidal representations of isolated syllables. Results are encouraging and show that with limited sets of bird species a recognizer based on this signal model may already be sufficient. 1.

Bird song can be divided into a sequence of syllabic elements. In this paper we investigate the possibility of bird species recognition based on the syllable pair histogram of the song. This representation compresses the variable-length syllable sequence into a fixed-dimensional feature vector. The histogram is computed by means of Gaussian syllable prototypes which are automatically found given the song data and the dissimilarity measure of syllables. Our representation captures the use of the syllable alphabet and also some temporal structure of the song. We demonstrate the method in bird species recognition with song patterns obtained from fifty individuals belonging to four common passerine bird species. 1.

ABSTRACT: Research on avian song learning has traditionally been based on an instructional model, as exemplified by the sensorimotor model of song development. Several large-scale, species-wide field studies of learned birdsongs have revealed that variation is narrowly restricted to certain aspects of song structure. Other aspects are sufficiently stereotyped and so widely shared by species ’ members that they qualify as species-specific universals. The limita-tions on natural song variation are difficult to recon-cile with a fully open, instructive model of song learn-ing. An alternative model based on memorization by selection postulates a system of innate neural templates that facilitate the recognition and rapid memorization of conspecific song patterns. Behavioral evi-dence compatible with this model includes learning preferences, rapid conspecific song learning, and widespread ocurrence of species-specific song universals that are recognized innately but fail to develop in songs of social isolates. A third model combines instruction, in the memorization phase, with selection during song production. An overproduced repertoire of plastic songs previously memorized by instruction is winnowed by selection imposed during social interactions at the time of adult song crystallization. Selec-tion during production is well established as a factor in the song development of several species, in the form of action-based learning. The possible role of selective processes in song memorization meritsfurther neurobiological investigation. � 1997 John Wiley & Sons,

Bird songs are fascinating acoustic phenomena. In this paper, we first examine the acoustic mechanisms of sound production in birds and contrast them with their anatomy. Next, we describe the simulation of a one-mass source together with a simple transmission line model for a psittacine bird. In concluding, we discuss future areas for research.

Abstract—This paper deals with the automated bird species identification problem, in which it is necessary to identify the species of a bird from its audio recorded song. This is a clever way to monitor biodiversity in ecosystems, since it is an indirect non-invasive way of evaluation. Different features sets which summarize in different aspects the audio properties of the audio signal are evaluated in this paper together with machine learning algorithms, such as probabilistic, instance-based, decision trees, neural networks and support vector machines. Experiments are conducted in a dataset of recorded songs of three bird species. The experimental results compare the performance of the features sets and different classifiers showing that it is possible to obtain very promising results in the automated bird species identification problem. Index Terms—machine learning; pattern recognition; signal processing; bird species identification I.

Abstract—In this paper, we present new probabilistic models for identifying bird species from audio recordings. We introduce the independent syllable model and consider two ways of aggregating frame level features within a syllable. We characterize each syllable as a probability distribution of its frame level features. The independent frame independent syllable (IFIS) model allows us to distinguish syllables whose feature distributions are different from one another. The Markov chain frame independent syllable (MCFIS) model is introduced for scenarios where the temporal structure within the syllable provides significant amount of discriminative information. We derive the Bayes risk minimizing classifier for each model and show that it can be approximated as a nearest neighbour classifier. Our experiments indicate that the IFIS and MCFIS models achieve 88.26 % and 90.61 % correct classification rates, respectively, while the equivalent SVM implementation achieves 86.15%. Keywords-Probabilistic modeling; audio classification; Bayesian inference; bird species identification; I.

On behalf of: