改进音频表示迁移，多层注意力探测提升生物声学识别效果
Multi-layer attentive probing improves transfer of audio representations for bioacoustics
- 采用多层与注意力机制探测头，捕捉时序信息增强表示能力
- 在BEANs和BirdSet上，多层探测使所有模型下游性能提升
- 注意力探测优于线性探测，尤其对Transformer模型效果显著
探测头将机器学习模型从音频中学习到的表示映射到下游任务标签，是评估表示学习的关键。当前多数生物声学基准使用固定、低容量的探测器，如最后一层编码器的线性层。这种标准化虽便于模型比较，但可能因忽略编码器特征与探测设计间的交互而产生偏差。本文系统研究了两个生物声学基准（BEANs 和 BirdSet）中的不同探测策略，对比了最后一层与多层探测，以及线性与注意力探测。结果表明，更大容量且利用时间信息的探测头表现更优。研究发现，仅依赖最后一层探测可能扭曲编码器真实质量。多层探测在所有测试模型上均提升下游任务性能，而注意力探测在Transformer模型上显著优于线性探测。
原文摘要 · Abstract (English)
Probing heads map the representations learned from audio by a machine learning model to downstream task labels and are a key component in evaluating representation learning. Most bioacoustic benchmarks use a fixed, low-capacity probe, such as a linear layer on the final encoder layer. While this standardization enables model comparisons, it may bias results by overlooking the interaction between encoder features and probe design. In this work, we systematically study different probing strategies across two bioacoustic benchmarks, BEANs and BirdSet. We evaluate last- and multi-layer probing, across linear and attention probes. We show that larger probe heads that leverage time information have superior performance. Our results suggest that current benchmarks may misrepresent encoder quality when relying on a last-layer probing setup. Multi-layer probing improves downstream task performance across all tested models, while attention probing has superior performance to linear probing for transformer models.
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