University of Salford

About: University of Salford is a education organization based out in Salford, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 13049 authors who have published 22957 publications receiving 537330 citations. The organization is also known as: University of Salford Manchester & The University of Salford Manchester.

Accessing the Community: Gaining Insider Perspectives From the Outside

Abstract: Accessing participants for research projects is often treated as unproblematic. However the experience outlined here of negotiating access to participants within a community setting illustrates the inherent difficulties of recruitment. The authors describe the techniques used and practical challenges faced when accessing participants within a socially deprived community for a qualitative research project on social capital. They used a number of different strategies to generate a diverse sample including advertising, snowballing, accessing gatekeepers, and street surveys. The value of a stakeholder analysis is described alongside issues surrounding the use of gatekeepers. Rather than acting as outsiders seeking participants at every available opportunity, a more fortuitous strategy involved the ethnographic approach of "being there" as active contributors to community life. Here, the cornerstones of credibility and trust were addressed in a process of continually negotiating access from a semi-insider position.

Automatic acoustic detection of birds through deep learning : the first bird audio detection challenge

Abstract: Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects. Multiple methods were able to attain performance of around 88% AUC (area under the ROC curve), much higher performance than previous general‐purpose methods. With modern machine learning including deep learning, general‐purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data with no manual recalibration, and no pre‐training of the detector for the target species or the acoustic conditions in the target environment.