![]() Therefore, all these 24 subclasses can be considered as a coarse, more general, “domestic alarm” class, but providing intra-class differentiation within it. These subclasses correspond to specific patterns of different domestic alarms such as bells or fire alarms. The pattern sounds class is made up of 24 subclasses. This dataset is composed by two coarse classes: pattern and unwanted sounds. Recently, a dataset that takes into account both limitations (OSR and FSL) has been made public by the authors. The main problem with these networks is that a relatively large amount of data is required to properly generalize FSL tasks, i.e., the need to consider many different classes even if only few samples are available per each class. The other approach lies on novel neural network architectures such as Siamese, facenet (trained with triplets) or on classical networks trained with novel loss functions such as ring loss or center loss. This prior-knowledge is usually represented by the use of a neural network pre-trained on external data that is employed as a feature extractor. On the one hand, the transfer learning (TL) approach tries to solve the problem of having only few samples by using prior knowledge. Two different approaches can be followed to tackle FSL. The goal of FSL would be to discern among the different bell types, even if all of them can be categorized into a general “bell” class. As an example, assume that a general class “bell” groups samples from different types of bells. A main feature of FSL has to do with the “intra-class” behavior of coarse categories. However, contributions in the audio domain are not so common and are mostly related to music fraud detection or speaker identification. FSL has been widely investigated in face recognition tasks. In fact, the 2019 edition incorporated an open-set recognition (OSR) task within the scope of ASC, where the idea was to classify an audio clip to a known scene type or to reject it when it belonged to an unknown scene.įew-shot learning (FSL) is another phenomenon related to real-world applications that aims to detect a specific pattern or class with little amount of data for training the classification system, i.e., using few examples per class. From its very first edition in 2013, different ASC and AEC tasks have been presented during the past years (2013, 2016, 20). ![]() This interest is also evidenced by the multiple editions of the successful international DCASE challenge (Detection and Classification of Acoustic Scenes and Events). The increase in research proposals related to these areas is motivated by the number of applications that can benefit from automation systems incorporating audio-based solutions, such as home assistants or autonomous driving. Acoustic event classification (AEC) and acoustic scene classification (ASC) are two areas that have grown significantly in the last years, often included within the machine listening field. Machine listening is the branch of artificial intelligence that aims to create intelligent systems that are capable of extracting relevant information from audio data. An extensive set of experiments is carried out considering multiple combinations of openness factors (OSR condition) and number of shots (FSL condition), showing the validity of the proposed approach and confirming superior performance with respect to a baseline system based on transfer learning. This paper proposes an audio OSR/FSL system divided into three steps: a high-level audio representation, feature embedding using two different autoencoder architectures and a multi-layer perceptron (MLP) trained on latent space representations to detect known classes and reject unwanted ones. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solutions aimed at addressing both limitations. Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training.
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