https://onlinelibrary.wiley.com/doi/epdf/10.1111/jav.01447

Automated birdsong recognition in complex acoustic environments: a review

Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context

Review of ensembles of multi-label classifiers: Models, experimental study and prospects

Using multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcome

/pdf/lifeclef-2016-multimedia-life-species-identification-4anl1hhh8x.pdf

LifeCLEF 2016: Multimedia Life Species Identification Challenges

Effective active learning strategy for multi-label learning

Birds have been widely used as biological indicators for ecological research. They respond quickly to environmental changes and can be used to infer about other organisms (e.g., insects they feed on). Traditional methods for collecting data about birds involves costly human effort. A promising alternative is acoustic monitoring. There are many advantages to recording audio of birds compared to human surveys, including increased temporal and spatial resolution and extent, applicability in remote sites, reduced observer bias, and potentially lower cost. However, it is an open problem for signal processing and machine learning to reliably identify bird sounds in real-world audio data collected in an acoustic monitoring scenario. Some of the major challenges include multiple simultaneously vocalizing birds, other sources of non-bird sound (e.g., buzzing insects), and background noise like wind, rain, and motor vehicles.

/pdf/the-9th-annual-mlsp-competition-new-methods-for-acoustic-4khrtcl7ds.pdf

Ken Larrey

Papers

The 9th annual MLSP competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment