Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction

Jin Jie Sean Yeo, Ee-Leng Tan, Jisheng Bai, Santi Peksi, Woon-Seng Gan
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Abstract

In this technical report, we describe the SNTL-NTU team's submission for Task 1 Data-Efficient Low-Complexity Acoustic Scene Classification of the detection and classification of acoustic scenes and events (DCASE) 2024 challenge. Three systems are introduced to tackle training splits of different sizes. For small training splits, we explored reducing the complexity of the provided baseline model by reducing the number of base channels. We introduce data augmentation in the form of mixup to increase the diversity of training samples. For the larger training splits, we use FocusNet to provide confusing class information to an ensemble of multiple Patchout faSt Spectrogram Transformer (PaSST) models and baseline models trained on the original sampling rate of 44.1 kHz. We use Knowledge Distillation to distill the ensemble model to the baseline student model. Training the systems on the TAU Urban Acoustic Scene 2022 Mobile development dataset yielded the highest average testing accuracy of (62.21, 59.82, 56.81, 53.03, 47.97)% on split (100, 50, 25, 10, 5)% respectively over the three systems.
利用教师启发的混淆类教学进行数据高效的声学场景分类
在本技术报告中,我们介绍了 SNTL-NTU 团队为 2024 年声学场景和事件检测与分类(DCASE)挑战赛任务 1 "数据高效低复杂度声学场景分类 "提交的报告。我们引入了三个系统来处理不同规模的训练分片。对于较小的训练分区,我们探索了通过减少基通道数量来降低所提供基线模型的复杂性。我们引入了混合形式的数据增强,以增加训练样本的多样性。对于较大的训练分裂,我们使用 FocusNet 为多个 Patchout faSt Spectrogram Transformer (PaSST) 模型和在 44.1 kHz 原始采样率上训练的基线模型的集合提供混淆类信息。我们使用知识蒸馏(Knowledge Distillation)技术将集合模型蒸馏为基线学生模型。在 TAU Urban Acoustic Scene 2022 Mobiledevelopment 数据集上对系统进行训练后,三个系统的平均测试准确率分别为(62.21, 59.82, 56.81, 53.03, 47.97)%和(100, 50, 25, 10, 5)%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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