Bioacoustic detection with wavelet-conditioned convolutional neural networks

Abstract

Many real-world time series analysis problems are characterized by low signal-to-noise ratios and compounded by scarce data. Solutions to these types of problems often rely on handcrafted features extracted in the time or frequency domain. Recent high-profile advances in deep learning have improved performance across many application domains; however, they typically rely on large data sets that may not always be available. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. We show that convolutional neural networks (CNNs), operating on wavelet transformations of audio recordings, demonstrate superior performance over conventional classifiers that utilize handcrafted features. Our key result is that wavelet transformations offer a clear benefit over the more commonly used short-time Fourier transform. Furthermore, we show that features, handcrafted for a particular dataset, do not generalize well to other datasets. Conversely, CNNs trained on generic features are able to achieve comparable results across multiple datasets, along with outperforming human labellers. We present our results on the application of both detecting the presence of mosquitoes and the classification of bird species.

Applications for deep learning in ecology

“…Deep learning can also be used with acoustic data such as bird songs (Knight et al, ; Potamitis, ; Salamon, Bello, Farnsworth, & Kelling, ), marine mammals vocalizations (Dugan, Clark, LeCun, & Van Parijs, ) and even mosquito sounds (Kiskin et al, ).…”

Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

“…The robustness of the system has been tested by classifying multiple species of insects simultaneously; 343 species, with a high success rate of 98.07%. The proposed method achieves superior results compared to previous methods [14][15][16][17], due to the proximity to a 100% classification. Moreover, this approach can be applied to other taxonomic groups adjusting the system parameters to the particularities of their sound emissions.…”

“…The fusion of coefficients increases the efficiency and reliability of the system making the proposed approach unique in terms of insect species recognition. Compared to previous works, this proposal is capable of identifying and classifying 343 species of either katydids crickets or cicadas on the level of specific species with higher success rates than previous systems, as shown in [14][15][16][17][18].…”

“…In [17], Continuous Wavelet Transformation (CWT) and Convolutional Neural Network (CNN) were used for mosquito detection and bird identification getting promising results. The 57 mosquito recordings used in this work were labeled with a success rate of 92.2% detecting the presence or absence of mosquitoes surpassing human labelers.…”

Automated detection of Hainan gibbon calls for passive acoustic monitoring

“…Deep learning holds enormous promise for automating the labelling of bioacoustic data. The number of applications is growing (Christin, Hervet, & Lecomte, 2019), but the majority of datasets are still labelled manually (Fairbrass et al, 2019;Kiskin et al, 2020;Pamula, Pocha, & Klaczynski, 2019), even as the rate of data collection makes this approach increasingly unsustainable. The mismatch between the potential of deep learning approaches and their actual uptake among practitioners occurs because getting models to perform as well as an experienced human is difficult.…”