37 lines
2.9 KiB
Markdown
37 lines
2.9 KiB
Markdown
---
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layout: single
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title: "Primate Subsegment Sorting"
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categories: research
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tags: bioacoustics audio-classification deep-learning data-labeling signal-processing
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excerpt: "Binary subsegment presorting improves noisy primate sound classification."
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header:
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teaser: /assets/figures/19_binary_primates_teaser.jpg
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scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
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---
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{:style="display:block; width:40%" .align-right}
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Automated acoustic classification plays a vital role in wildlife monitoring and bioacoustics research. This study introduces a sophisticated pre-processing and training strategy to significantly enhance the accuracy of multi-class audio classification, specifically targeting the identification of different primate species from field recordings.
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A key challenge in bioacoustics is dealing with datasets containing weak labels (where calls of interest occupy only a portion of a labeled segment), varying segment lengths, and poor signal-to-noise ratios (SNR). Our approach addresses this by:
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1. **Subsegment Analysis:** Processing audio recordings represented as **MEL spectrograms**.
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2. **Refined Labeling:** Meticulously **relabeling subsegments** within the spectrograms. This "binary presorting" step effectively identifies and isolates the actual vocalizations of interest within longer, weakly labeled recordings.
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3. **CNN Training:** Training **Convolutional Neural Networks (CNNs)** on these refined, higher-quality subsegment inputs.
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4. **Data Augmentation:** Employing innovative **data augmentation techniques** suitable for spectrogram data to further improve model robustness.
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<center>
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<img src="/assets/figures/19_binary_primates_thresholding.jpg" alt="Visualization related to the thresholding or selection process for subsegment labeling" style="display:block; width:70%">
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<figcaption>Thresholding or selection criteria for subsegment refinement.</figcaption>
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</center><br>
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The effectiveness of this methodology was evaluated on the challenging **ComParE 2021 Primate dataset**. The results demonstrate remarkable improvements in classification performance, achieving substantially higher accuracy and Unweighted Average Recall (UAR) scores compared to existing baseline methods.
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<center>
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<img src="/assets/figures/19_binary_primates_results.jpg" alt="Graphs or tables showing improved classification results (accuracy, UAR) compared to baselines" style="display:block; width:70%">
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<figcaption>Comparative performance results on the ComParE 2021 dataset.</figcaption>
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</center><br>
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This work represents a significant advancement in handling difficult, real-world bioacoustic data, showcasing how careful data refinement prior to deep learning model training can dramatically enhance classification outcomes. {% cite koelle23primate %}
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