# Correlation Clustering of Bird Sounds

arXiv: https://arxiv.org/abs/2306.09906 · HTML: https://mlsanity.com/paper/2306.09906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts](https://mlsanity.com/paper/1505.06973/) (2015)
- [Improving Bird Classification with Unsupervised Sound Separation](https://mlsanity.com/paper/2110.03209/) (2021)
- [Pairwise Supervision Can Provably Elicit a Decision Boundary](https://mlsanity.com/paper/2006.06207/) (2020)
- [End-to-end Learning for Graph Decomposition](https://mlsanity.com/paper/1812.09737/) (2018)
- [Multicuts and Perturb & MAP for Probabilistic Graph Clustering](https://mlsanity.com/paper/1601.02088/) (2016)
- [Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches](https://mlsanity.com/paper/2007.03123/) (2020)

## Abstract (arXiv)

Bird sound classification is the task of relating any sound recording to those species of bird that can be heard in the recording. Here, we study bird sound clustering, the task of deciding for any pair of sound recordings whether the same species of bird can be heard in both. We address this problem by first learning, from a training set, probabilities of pairs of recordings being related in this way, and then inferring a maximally probable partition of a test set by correlation clustering. We address the following questions: How accurate is this clustering, compared to a classification of the test set? How do the clusters thus inferred relate to the clusters obtained by classification? How accurate is this clustering when applied to recordings of bird species not heard during training? How effective is this clustering in separating, from bird sounds, environmental noise not heard during training?