An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

The problem of training a deep neural network with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications are those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research in this area, results with two baseline systems (one trained from scratch and another based on transfer learning), are presented.利用少量正样本训练深度神经网络的问题，被称为少样本学习（Few-Shot Learning, FSL）。众所周知，传统深度学习（Deep Learning, DL）算法在大规模数据集上训练时通常可取得优异性能。然而在诸多实际应用场景中，难以获取如此海量的训练样本。在图像领域中，少样本学习的典型应用场景涵盖人脸识别相关任务；在音频领域，音乐欺诈检测与说话人识别等任务均可通过少样本学习方法获得性能提升。本文聚焦于利用少量样本，将少样本学习方法应用于各类警报声（如门铃、火灾警报）所产生的特定有意声学事件的检测任务。这类警报声通常出现在家庭环境中，而该类环境下存在大量覆盖各类声音类别的声学事件，因此在实际场景中对这类警报声进行检测，可被视作开放集识别（Open-Set Recognition, OSR）问题。针对音频少样本学习领域缺乏专用公开数据集的现状，研究人员通常会对现有其他数据集进行改造以满足需求。本文旨在为音频识别领域的研究者提供一个经过精细标注的、适用于少样本学习与开放集识别任务的数据集：该数据集包含34个类别共1360条音频片段，分为目标模式声与干扰声两类。为推动该领域的研究发展，本文还给出了两个基线系统的测试结果——其一为从零开始训练的模型，其二为基于迁移学习的模型。