# Improving Primate Sounds Classification using Binary Presorting for Deep Learning

arXiv: https://arxiv.org/abs/2306.16054 · HTML: https://mlsanity.com/paper/2306.16054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [Convolutional Recurrent Neural Networks for Music Classification](https://mlsanity.com/paper/1609.04243/) (2016)
- [Rethinking CNN Models for Audio Classification](https://mlsanity.com/paper/2007.11154/) (2020)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [Automatic tagging using deep convolutional neural networks](https://mlsanity.com/paper/1606.00298/) (2016)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization](https://mlsanity.com/paper/1912.04761/) (2019)
- [Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms](https://mlsanity.com/paper/1703.01789/) (2017)
- [Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network](https://mlsanity.com/paper/1706.02291/) (2017)
- [An Ensemble of Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2007.07966/) (2020)
- [Learning Multiscale Features Directly From Waveforms](https://mlsanity.com/paper/1603.09509/) (2016)
- [AclNet: efficient end-to-end audio classification CNN](https://mlsanity.com/paper/1811.06669/) (2018)
- [Music Artist Classification with Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1901.04555/) (2019)
- [Learning with Out-of-Distribution Data for Audio Classification](https://mlsanity.com/paper/2002.04683/) (2020)
- [Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning](https://mlsanity.com/paper/2006.03429/) (2020)
- [Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification](https://mlsanity.com/paper/2101.10390/) (2021)
- [Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms](https://mlsanity.com/paper/2008.04590/) (2020)
- [Pseudo strong labels for large scale weakly supervised audio tagging](https://mlsanity.com/paper/2204.13430/) (2022)
- [Acoustic Leak Detection in Water Networks](https://mlsanity.com/paper/2012.06280/) (2020)
- [Visual Transformers for Primates Classification and Covid Detection](https://mlsanity.com/paper/2212.10093/) (2022)

## Abstract (arXiv)

In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging textitComparE 2021 dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines.