BirdSetEfficientNetB1: Bioacoustic Classification Model

• BirdSetEfficientNetB1 is a specialized bioacoustic model that adapts EfficientNet B1 to classify 5-second audio segments into 9736 species with fine-grained predictions and 1280-dimensional embeddings.
• The model achieves high accuracy in CPU-constrained environments, ranking top in the BirdCLEF+ 2025 challenge with robust ROC-AUC scores.
• It integrates into the BirdSet benchmark for large-scale biodiversity monitoring and supports transfer learning for enhanced environmental and conservation research.

BirdSetEfficientNetB1 is a specialized bioacoustic classification model derived from the EfficientNet B1 architecture and integrated into the BirdSet benchmark and the broader Bioacoustics Model Zoo. It is designed to process 5-second audio segments, outputting both fine-grained class predictions and high-dimensional feature embeddings for large-scale avian and multi-species audio classification. BirdSetEfficientNetB1 has attained high performance, particularly in CPU-constrained environments, as evidenced by its top ranking in the BirdCLEF+ 2025 challenge (2507.08236). Its architecture and deployment pipeline reflect recent advances in bioacoustic modeling, reproducible benchmarking, and efficient inference.

1. Model Overview and Architecture

BirdSetEfficientNetB1 builds upon the EfficientNet B1 convolutional neural network but is adapted to the requirements of large-scale avian sound classification:

• Input Format: Processes 5-second audio clips, partitioned internally into 12 time frames ("rows"), matching the common segmentation protocol for bioacoustic soundscapes (2507.08236).
• Output Heads:
  • Class Prediction Layer: A dense layer with 9736 output nodes (corresponding to a large taxonomy of bird and animal species), utilizing sigmoid activations for multi-label inference.
  • Embedding Head: Produces a 1280-dimensional feature embedding per clip, supporting transfer learning and downstream tasks.
• Model Configuration Table:

Parameter	Value
Clip Length	5 s
Step Size	5 s
Rows	12
Predict	9736
Embed	1280

A native 5-second window aligns exactly with segment-based testing protocols in open soundscape evaluation, avoiding the need for further temporal aggregation or sliding windows.

2. Integration in Bioacoustic Benchmarks

BirdSetEfficientNetB1 is a principal backbone within BirdSet, a large-scale benchmark dataset for avian bioacoustics (2403.10380). The BirdSet pipeline involves:

• Audio Preprocessing: Raw audio is event-detected and segmented, converted to log-Mel spectrograms using libraries (e.g., Librosa), optionally with data augmentation (mixup, noise addition) for generalization.
• Vision-based Processing: Preprocessed spectrograms, shaped as image-like tensors, are fed into the EfficientNetB1 backbone for feature extraction and classification.

This approach leverages EfficientNet's parameter efficiency to enable large-scale multi-label classification for thousands of species on both in-distribution (focal) and real-world (soundscape) test sets.

3. Performance in BirdCLEF+ 2025 and Model Zoo Context

Within the BirdCLEF+ 2025 challenge, which imposed stringent 90-minute CPU-only inference limits, BirdSetEfficientNetB1 achieved the following:

• Public Leaderboard ROC-AUC: 0.810
• Private Leaderboard ROC-AUC: 0.778
• Inference Time: Mean of 2.21 seconds per file, corresponding to ~26 minutes for a 700-file test set, well below the allotted time.

Compared to other zoo backbones:

• BirdNET: ROC-AUC in the low 0.72 range (with optimizations)
• Perch (TFLite optimized): Public 0.729, Private 0.711
• BirdSetConvNeXT: Public 0.768, Private 0.756

BirdSetEfficientNetB1 thus combines high predictive performance with viable runtime for CPU-only, large-batch evaluation (2507.08236).

4. Inference and Optimization Techniques

A distinguishing aspect is BirdSetEfficientNetB1’s minimal reliance on explicit optimization for CPU inference:

• Models such as Perch required architecture rewrites and framework conversion (e.g., TensorFlow to TFLite), leading to near 10x speedups to meet challenge constraints.
• BirdSetEfficientNetB1 operated efficiently within constraints with its base PyTorch implementation, without extensive modification.

This suggests the design choices—specialized for bioacoustic clip length and streamlined output—enable practical deployment on standard hardware, beneficial for both scientific studies and citizen science applications.

5. Training Workflow and Loss Function

Training on BirdSet involves:

1. Segmentation: Audio split into non-overlapping 5-second intervals.
2. Feature Construction: Waveforms are converted into log-Mel spectrogram images.
3. Model Training: EfficientNetB1 is initialized with ImageNet weights and fine-tuned on BirdSet, with the final dense layer replaced for 9736-way output.
4. Multi-label Loss: The classification loss is typically binary cross-entropy:

$$\mathcal{L} = -\sum_{i=1}^{C} \left[ y_i \log (\hat{y}_i) + (1-y_i) \log (1-\hat{y}_i) \right],$$

where $C$ = number of classes, $y_i$ = target, $\hat{y}_i$ = predicted probability.

• Sample Implementation:

from tensorflow.keras.applications import EfficientNetB1 from tensorflow.keras import layers, models base_model = EfficientNetB1(weights='imagenet', include_top=False, input_shape=(height, width, 3)) x = layers.GlobalAveragePooling2D()(base_model.output) x = layers.Dropout(0.3)(x) output = layers.Dense(num_classes, activation='sigmoid')(x) model = models.Model(inputs=base_model.input, outputs=output)

This snippet demonstrates end-to-end integration for segment-level bioacoustic classification, as outlined in (2403.10380).

6. Practical Applications and Deployment

BirdSetEfficientNetB1 is well-suited for:

• Automated Acoustic Monitoring: Large-scale biotic inventories in natural soundscapes, supporting research in biodiversity and conservation.
• Field Deployments: Robustness to covariate shift between curated (focal) and real-world (soundscape) data, evidenced by strong leaderboard performance under domain shift.
• Transfer Learning: The 1280-dim embeddings facilitate transfer to new tasks or species not covered in the initial taxonomy.

Its design makes it fit for environments where only CPU-based inference is possible, reflecting needs in low-power field recorders and decentralized bioacoustic data collection campaigns.

7. Comparative Perspective and Research Significance

The parameter-efficient, segment-optimized implementation underlying BirdSetEfficientNetB1 positions it advantageously relative to traditional image or acoustic CNN backbones and even some transformer-based models. Its competitive cmAP and AUROC, resource efficiency, and minimal adaptation requirement for deployment explain its adoption in recent large-scale challenges and benchmarks (2403.10380, 2507.08236). A plausible implication is that models explicitly co-designed with dataset segmentation and task structure can outperform architectures relying on post-hoc windowing or aggregation.

In summary, BirdSetEfficientNetB1 exemplifies recent trends in robust, scalable, and reproducible machine learning for environmental sound analysis, balancing accuracy with practical constraints in real-world bioacoustic monitoring and research.