Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Sign and share: What influences our participation in online microvolunteering

Microvolunteering is bite-size volunteering with no commitment to repeat and minimum formality, involving short and specific actions. Online microvolunteering occurs through an internet-connected device. University students' online microvolunteering decisions were investigated using an extended theory of planned behavior (TPB) comprising attitudes and normative and control perceptions, with the additional variables of moral norm and group norm. Participants (N = 303) completed the main TPB questionnaire and 1-month follow-up survey (N = 171) assessing engagement in online microvolunteering. Results generally supported standard and additional TPB constructs predicting intention. Intention predicted behavior. The findings suggest an important role for attitudes and moral considerations in understanding what influences this increasingly popular form of online activity.