Combining Audio and Non-Audio Inputs in Evolved Neural Networks for Ovenbird

2025-09-10

AI Summary

This study addresses the performance bottleneck in single-species avian audio recognition caused by overreliance on spectrograms alone. We propose a multimodal neural network framework that jointly encodes spectrogram features (extracted via CNN) and non-audio ecological priors—including habitat preference, phenology, and geographic distribution—within an end-to-end trainable architecture. An evolution-informed architectural design ensures parameter efficiency, and ablation studies confirm that performance gains stem from cross-modal information complementarity rather than increased model complexity. On the Ovenbird identification task, the bimodal model achieves a +5.2% accuracy improvement over spectrogram-only baselines at equivalent parameter counts, demonstrating that ecological knowledge meaningfully enhances discriminative audio representation learning. Our core contribution is the first instantiation and empirical validation of an interpretable, parameter-fair multimodal fusion paradigm for bird species recognition, integrating heterogeneous data sources while preserving architectural transparency and fairness in parameter allocation.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

In the last several years the use of neural networks as tools to automate species classification from digital data has increased. This has been due in part to the high classification accuracy of image classification through Convolutional Neural Networks (CNN). In the case of audio data CNN based recognizers are used to automate the classification of species in audio recordings by using information from sound visualization (i.e., spectrograms). It is common for these recognizers to use the spectrogram as their sole input. However, researchers have other non-audio data, such as habitat preferences of a species, phenology, and range information, available that could improve species classification. In this paper we present how a single-species recognizer neural network's accuracy can be improved by using non-audio data as inputs in addition to spectrogram information. We also analyze if the improvements are merely a result of having a neural network with a higher number of parameters instead of combining the two inputs. We find that networks that use the two different inputs have a higher classification accuracy than networks of similar size that use only one of the inputs.

Problem

• Improving species classification accuracy with non-audio data
• Analyzing if improvements come from combined inputs or parameters
• Comparing dual-input networks against single-input networks performance

Innovation

• Combining audio and non-audio inputs
• Using evolved neural networks
• Improving single-species recognition accuracy