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This paper describes a newly-launched public evaluation challenge on acoustic scene classification and detection of sound events within a scene. Systems dealing with such tasks are far from exhibiting human-like performance and robustness. Undermining factors are numerous: the extreme variability of sources of interest possibly interfering, the presence of(More)
In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range(More)
Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce(More)
—We describe a non-parametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning. The estimator uses an adaptive partitioning method and runs in Θ N log N time, with low memory requirements. In experiments using known distributions, the estimator is several(More)
An increasing number of researchers work in computational auditory scene analysis (CASA). However, a set of tasks, each with a well-defined evaluation framework and commonly used datasets do not yet exist. Thus, it is difficult for results and algorithms to be compared fairly, which hinders research on the field. In this paper we will introduce a(More)
Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on(More)
We describe a new method for preprocessing STFT phase-vocoder frames for improved performance in real-time onset detection, which we term " adaptive whitening ". The procedure involves normalising the magnitude of each bin according to a recent maximum value for that bin, with the aim of allowing each bin to achieve a similar dynamic range over time, which(More)
In this submission we offer for evaluation several audio feature extraction plugins in Vamp format. Some of these plugins represent efficient implementations based on modern work, while others are no longer state-of-the-art and were developed a few years ago. The methods implemented in this set of plugins are described in the literature and are referenced(More)
Harmonic birdsong is often highly nonstationary, which suggests that standard FFT representations may be of limited suitability. Wavelet and chirplet techniques exist in the literature, but are not often applied to signals such as bird vocalisations, perhaps due to analysis complexity. In this paper we develop a single-scale chirp analysis (computationally(More)