A deafening silence: a lack of data and reproducibility in published bioacoustics research?

Baker, Edward; Vincent, Sarah

doi:10.3897/bdj.7.e36783

Cited by 15 publications

(9 citation statements)

References 20 publications

(21 reference statements)

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“…Concerns have been raised about a lack of reproducibility in bioacoustic research that may be impeding progress 41 . The present study uses a published open-source CNN architecture, a publicly-released richly annotated birdsong dataset and additional code made available in a repository 42 to encourage other researchers to replicate the present results.…”

Section: Discussionmentioning

confidence: 99%

Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

Sanchez

Hossain

English

et al. 2021

Sci Rep

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The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

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Section: Discussionmentioning

confidence: 99%

Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

Sanchez

Hossain

English

et al. 2021

Sci Rep

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“…Software based automation in acoustic analysis is an important step in ensuring reproducible results, which in turns spurs the growth of the research field (McFee et al 2018;Baker and Vincent 2019). The itsfm package written in the Python (Van Rossum and Drake Jr 1995) language is an open-sourced method which may be used in the analysis of animal vocalisations such as CF-FM bat calls, and other vocalisations.…”

Section: Discussionmentioning

confidence: 99%

“…While code descriptions help explaining the principles behind design, it is not sufficient to ensure uniformity or correctness in implementation. Differences in implementation may lead to differences in scientific results (Baker and Vincent 2019; McFee et al 2018). Publishing code as publicly available packages allows for external code inspection and improvements.…”

Section: Introductionmentioning

confidence: 99%

itsfm, an open-source package to reliably segment and measure sounds by frequency modulation

Beleyur

2021

Preprint

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Analysing animal vocalisations in detail provides insights into the biomechanics, decision making and sensory processes behind their behaviours. Echolocating bats, and in particular, the CF-FM calls of high-duty cycle bats serve as a convenient model system to illustrate this point. The CF component in the CF-FM call is used for prey detection and the FM component is used in target ranging. According to the behavioural context at hand such as flight with conspecifics or prey capture, bats choose to increase the duration, intensity or spectral range of the components differently. Studying the call component alterations requires an objective methodology that first segments the components and then allows measurements on them. Studies till now have segmented the call components manually, or automatically using what I term the ‘peak-frequency’ method. Manual segmentation is error prone, while the ‘peak-frequency’ method requires on-axis recordings for good results. Despite multiple papers using a peak-frequency based segmentation, there remain no publicly available software implementations. itsfm is an open-source package that fills this gap with two implemntations that can segment CF-FM calls, one of them being an implementation of the peak-percentage method. itsfm additionally introduces the ‘pseudo-Wigner-Ville distribution’ (PWVD) method for call segmentation, thus allowing the segmentation of calls captured under a wider variety of recording conditions. I create a synthetic dataset and assess the performance of the PWVD method and the ‘peak-frequency’ method. The PWVD performs consistently well in call component segmentation in comparison to the peak-percentage method. I also discuss the supporting methods in the itsfm package that can help the further automatic segmentation, measurement and analysis of sounds. Though originally developed for the segmentation and measurement of CF-FM bat calls, the methods in itsfm are speciesagnostic, and may be used for vocalisations of any type.

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“…Examples include bird [52,56,32,17,16] and bat [89,65] species classification. Like their image counterparts, these datasets can be challenging to collect and accurately annotate [8]. These annotation issues can also be compounded by factors such as background noise and low quality recordings.…”

Section: Image Audio and Video Datasetsmentioning

confidence: 99%

Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset

Horn

Qian

Wilber

et al. 2022

Lecture Notes in Computer Science

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We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on images, the counterparts in audio and video finegrained categorization are relatively unexplored. To encourage advancements in this space, we have carefully constructed the SSW60 dataset to enable researchers to experiment with classifying the same set of categories in three different modalities: images, audio, and video. The dataset covers 60 species of birds and is comprised of images from existing datasets, and brand new, expert curated audio and video datasets. We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods. Our findings show that performance of audiovisual fusion methods is better than using exclusively image or audio based methods for the task of video classification. We also present interesting modality transfer experiments, enabled by the unique construction of SSW60 to encompass three different modalities. We hope the SSW60 dataset and accompanying baselines spur research in this fascinating area.

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A deafening silence: a lack of data and reproducibility in published bioacoustics research?

Cited by 15 publications

References 20 publications

Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

itsfm, an open-source package to reliably segment and measure sounds by frequency modulation

Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset

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