In this chapter, I show how deep metric learning (DML) methods can be applied for bioacoustic classification, specifically with regard to the use of unlabeled acoustic data. The availability of labeled data in bioacoustic applications is usually scarce, which hampers the performance of standard classification models and requires methods to effectively use large volumes of unlabeled recording. When implementing contrastive embedding methods within a DML framework, which is the main goal of the current study, we created a reliable system capable of learning expressive representations of acoustic signals. These representations make the correct categorization of the species and types of sound possible, especially where there is limited labeling. One of the main novelties of our work is the data augmentation techniques adapted for bioacoustic data, such as time stretching, pitch shift, and noise injection. These augmentations imitate real-world variations, which makes the resulting embeddings more invariant. To verify our framework, we performed experiments with publicly available bioacoustic datasets with special attention paid to both supervised and semi-supervised contexts. The evaluation criteria of accuracy, clustering quality, and resistance to noise show that the proposed model outperforms the baseline approach. Based on our study, we highlight DML and contrastive embeddings as techniques that can address the issues of data dearth and variability provoked by bioacoustic classification. This work not only advances classification performance, but also creates potential for the routine application of bioacoustic monitoring systems across various ecosystems. Because our approach refutes the need for extensive labeled data, it greatly reduces the threshold for implementing machine learning for ecological and conservation use. They create the premise for actual-time automatic analysis of bioacousticians in biodiversity analysis and preservation across the world.