Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Organizational affordances: A structuration theory approach to affordances

Affordance is an important concept in the field of human–computer interaction. There are various interpretations of affordances, often extending the original notion of James J. Gibson. Often the treatment of affordances in the current human–computer interaction literature has been a one-to-one relationship between a user and an artefact. We believe that the social and cultural contexts within which an artefact is situated affect the way in which the artefact is used and the notion of affordance needs to be seen as a dynamic, always emerging relationship between people and their environment. Using a Structuration Theory approach, we conceptualize the notion of affordance at a much broader level, encompassing social and cultural aspects. We suggest that affordances should be seen at three levels: single user, organizational (or work group) and societal. Focusing on the organizational level affordances, we provide details of several important factors that affect the emergence of affordances. - This article provides a new perspective on the discourse of affordance with the use of Structuration Theory. - It shows how affordance can be understood as ‘use’ in situated practices (i.e. ‘technology-in-practice’) - The Structuration Theory approach to affordances is showcased using two case studies.