Deep neural network models have become the dominant approach to a large variety of tasks within music information retrieval (MIR). These models generally require large amounts of (annotated) training data to achieve high accuracy. Because not all applications in MIR have sufficient quantities of training data, it is becoming increasingly common to transfer models across domains. This approach allows representations derived for one task to be applied to another, and can result in high accuracy with less stringent training data requirements for the downstream task. However, the properties of pre-trained audio embeddings are not fully understood. Specifically, and unlike traditionally engineered features, the representations extracted from pre-trained deep networks may embed and propagate biases from the model's training regime. This work investigates the phenomenon of bias propagation in the context of pre-trained audio representations for the task of instrument recognition. We first demonstrate that three different pre-trained representations (VGGish, OpenL3, and YAMNet) exhibit comparable performance when constrained to a single dataset, but differ in their ability to generalize across datasets (OpenMIC and IRMAS). We then investigate dataset identity and genre distribution as potential sources of bias. Finally, we propose and evaluate post-processing countermeasures to mitigate the effects of bias, and improve generalization across datasets.

翻译：深度神经网络模型已成为音乐信息检索（MIR）领域多种任务的主流方法。这类模型通常需要大量（标注）训练数据才能实现高精度。由于MIR中并非所有应用都具备充足训练数据，跨领域迁移模型日益普遍。该方法允许为某项任务构建的表征应用于另一任务，并能在降低下游任务训练数据需求的同时达到高精度。然而，预训练音频嵌入的特性尚未被完全理解。具体而言，与传统人工设计的特征不同，从预训练深度网络中提取的表征可能嵌入并传播模型训练过程中的偏差。本研究针对乐器识别任务，探究预训练音频表征中的偏差传播现象。我们首先证明三种不同的预训练表征（VGGish、OpenL3和YAMNet）在单一数据集上表现相当，但跨数据集（OpenMIC与IRMAS）泛化能力存在差异。随后，我们将数据集身份和流派分布作为潜在偏差来源进行探究。最后，我们提出并评估了后处理对抗措施以缓解偏差影响，并提升跨数据集泛化能力。