Digital Bioacoustics

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Welcome to c/DigitalBioacoustics, a unique niche in the vast universe of online forums and digital communities. At its core, bioacoustics is the study of sound in and from living organisms, an intriguing intersection of biology and acoustics. Digital bioacoustics, an extension of this field, involves using technology to capture, analyze, and interpret these biological sounds. This community is dedicated to exploring these fascinating aspects of nature through a digital lens.

As you delve into c/DigitalBioacoustics, you'll notice it's not just another technical forum. This space transcends the usual drone of server rooms or the monotonous tap-tap of keyboards. Here, members engage in a unique fusion of natural wonders and technological prowess. Imagine a world where the rustling of leaves, the chirping of birds, and the mysterious calls of nocturnal creatures meet the precision of digital recording and analysis.

Within this domain, we, the participants, become both observers and participants in an intricate dance. Our mission is to unravel the mysteries of nature's soundtrack, decoding the language of the wild through the lens of science. This journey is not just about data and graphs; it's about connecting with the primal rhythm of life itself.

As you venture deeper, the poetic essence of our community unfolds. Nature's raw concert, from the powerful songs of mating calls to the subtle whispers of predator and prey, creates a tapestry of sounds. We juxtapose these organic melodies with the mechanical beeps and buzzes of our equipment, a reminder of the constant interplay between the natural world and our quest to understand it.

Our community embodies the spirit of curious scientists and nature enthusiasts alike, all drawn to the mystery and majesty of the natural world. In this symphonic melding of science and nature, we discover not just answers, but also new questions and a deeper appreciation for the complex beauty of our planet.

c/DigitalBioacoustics is more than a mere digital gathering place. It's a living, breathing symphony of stories, each note a discovery, each pause a moment of reflection. Here, we celebrate the intricate dance of nature and technology, the joy of discovery, and the enduring quest for understanding in a world filled with both harmony and dissonance.

For those brave enough to explore its depths, c/DigitalBioacoustics offers a journey like no other: a melding of science and art, a discovery of nature's secrets, and a celebration of the eternal dance between the wild and the wired.

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Please let me know if you know of any other related communities or any other links I should add.

founded 2 years ago

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Haggunenons

Multi-label classification of frog species via deep learning (www.researchgate.net)

submitted 2 years ago by Haggunenons to c/digitalbioacoustics

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[–] Haggunenons 1 points 2 years ago* (last edited 2 years ago)

Summary made by Quivr/gpt-3.5-turbo-0613

The document titled "Multi-label classification of frog species via deep learning" discusses a method for classifying multiple frog species in audio recordings. The study aims to overcome the limitation of previous methods that assume recordings contain only one species.

The method consists of four steps: data description, signal pre-processing, feature extraction, and classification. In the data description step, digital recordings of frog calls were obtained using a specialized device. The recordings were two-channel and sampled at 22.05 kHz.

In the signal pre-processing step, the recordings were resampled and converted to mono. Three different time-frequency representations were tested: fast-Fourier transform spectrogram, constant-Q transform spectrogram, and Gammatone-like spectrogram.

For feature extraction, a deep learning algorithm was used to extract important features from the time-frequency representations. This approach was preferred over traditional hand-crafted features, as deep learning has shown better classification performance in frog call classification.

Finally, a binary relevance based multi-label classification approach was used to classify simultaneously vocalizing frog species. Eight frog species from Queensland, Australia, were selected for the experiment.

The document concludes by mentioning that features extracted using deep learning can achieve better classification performance compared to hand-crafted features.