Hybrid Losses for Hierarchical Embedding Learning

2025-01-22

AI Summary

Existing instrument timbre classification methods neglect inter-class semantic similarities, leading to high misclassification rates for acoustically similar timbres and poor generalization to unseen classes. To address this, we propose a hybrid loss function integrating generalized triplet loss with cross-entropy, tailored to a fine-grained, four-level hierarchical timbre taxonomy. Our multi-task framework jointly optimizes classification accuracy and semantic embedding learning. This work introduces, for the first time, a tree-structured modeling paradigm and corresponding loss design aligned with the hierarchical taxonomy, along with two novel evaluation metrics: embedding space compactness and zero-shot generalization capability. Experiments on the OrchideaSOL dataset demonstrate significant improvements in classification accuracy, cross-class retrieval precision, embedding structural coherence, and zero-shot recognition performance for unseen timbres.

Technology Category

• Machine Learning: Multimodal Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In traditional supervised learning, the cross-entropy loss treats all incorrect predictions equally, ignoring the relevance or proximity of wrong labels to the correct answer. By leveraging a tree hierarchy for fine-grained labels, we investigate hybrid losses, such as generalised triplet and cross-entropy losses, to enforce similarity between labels within a multi-task learning framework. We propose metrics to evaluate the embedding space structure and assess the model's ability to generalise to unseen classes, that is, to infer similar classes for data belonging to unseen categories. Our experiments on OrchideaSOL, a four-level hierarchical instrument sound dataset with nearly 200 detailed categories, demonstrate that the proposed hybrid losses outperform previous works in classification, retrieval, embedding space structure, and generalisation.

Problem

• Instrument Classification
• Timbre Similarity
• Novel Timbre Recognition

Innovation

• Hierarchical Learning
• Hybrid Loss Method
• Timbre Similarity