Abstract
Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.
Highlights
More than 13% (1,373) of bird species are vulnerable or in danger of extinction from causes such as deforestation, introduction of alien species, and global climate change (International Union for the Conservation of Nature Red Data List, 2014)
The main measurement of true interest in denoising is the Signal-to-Noise Ratio (SNR), which can be calculated by dividing the power of the signal (S) by the power of noise (N), as given in Eq 4, which is in units of decibels
We implemented our algorithm in Matlab using the Wavelet Toolbox, which is a comprehensive toolbox for wavelet analysis
Summary
More than 13% (1,373) of bird species are vulnerable or in danger of extinction from causes such as deforestation, introduction of alien species, and global climate change (International Union for the Conservation of Nature Red Data List, 2014). If the noise occupies high frequencies while the bird of interest sings low frequency songs this would be sufficient to eliminate noise, but since the spectra of the noise and the signal overlap, this is not the case Another traditional approach is the Wiener filter, which generates an estimate of the desired or target random (Gaussian) process based on linear time-invariant filtering and the minimum mean square error between the estimated signal and the desired signal by assuming that the signal and noise are stationary and spectral information is available [14]. Another concern when denoising birdsong is the effect of overlapping bird calls To test this issue, we selected ten examples of recodings that contained overlapping songs from different combinations of species.
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