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ShareScore release 0.9.0
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2 results for “open-set”
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
<p>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.</p> <p> </p>
Open-Set Tagging Dataset (OST)
<p>Open-set Tagging (OST) is a synthetic dataset of 1s clips used to evaluate source-centric representation learning models in the paper <a href="https://ieeexplore.ieee.org/document/10890242">Compositional Audio Representation Learning</a>.</p> <p>Due to the size of the dataset, we only share the source files, and provide the scripts to generate the dataset are available <a href="https://github.com/sripathisridhar/moads">here.</a><br><br>The dataset generation process is as follows:<br>1. From single-source FSD50K audio files, we generate a dataset of 10s soundscapes called Open-set Soundscapes (OSS) using <a href="https://github.com/justinsalamon/scaper">Scaper</a>.</p> <p>2. We then center a 1s window around the center of each sound event in the 10s soundscapes to generate Open-set Tagging (OST), which contains ~500k clips. </p> <p>If you are not going to use OSS, you can choose to synthesize it without audio-- this will synthesize only the <a href="https://github.com/marl/jams">JAMS</a> annotation files needed for the 1s clips. Using the OSS JAMS files, OST clips can be generated deterministically.</p> <p>There are five dataset variants (~17GB each), each with a different random assignment of classes to the known and unknown class categories. For further details, refer to our previous paper <a href="https://dcase.community/documents/workshop2023/proceedings/DCASE2023Workshop_Sridhar_11.pdf">Multi-label open-set audio classification</a>. In <a href="https://ieeexplore.ieee.org/document/10890242">this work</a>, OST dataset variant 1 is referred to as OST for simplicity. <br><br>We also introduce a tiny version of the dataset called OST-Tiny, which contains ~20k clips and only 10 known classes. This is convenient for faster prototyping and to evaluate models in a more challenging open-set classification scenario.</p> <p> </p>
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