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

Naranjo-Alcazar, Javier; Perez-Castanos, Sergi; Zuccarrello, Pedro; Torres, Ana; Lopez, Jose J.; Ferri, Franscesc J.; Cobos, Máximo

doi:10.48550/arxiv.2002.11561

Cited by 2 publications

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“…Two different autoencoder architectures with a multi-layer perceptron classifier are designed to identify target sound classes and reject unwanted ones. The dataset, consisting of sounds in domestic environments, has been created ad hoc by the authors and structured for few-shot learning applications [43].…”

Section: Related Workmentioning

confidence: 99%

Few-Shot Emergency Siren Detection

Cantarini

Gabrielli

Squartini

2022

Sensors

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It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This paper proposes a workflow based on few-shot metric learning for emergency siren detection performed in steps: prototypical networks are trained on publicly available sources or synthetic data in multiple combinations, and at inference time, the best knowledge learned in associating a sound with its class representation is transferred to identify ambulance sirens, given only a few instances for the prototype computation. Performance is evaluated on siren recordings acquired by sensors inside and outside the cabin of an equipped car, investigating the contribution of filtering techniques for background noise reduction. The results show the effectiveness of the proposed approach, achieving AUPRC scores equal to 0.86 and 0.91 in unfiltered and filtered conditions, respectively, outperforming a convolutional baseline model with and without fine-tuning for domain adaptation. Extensive experiments conducted on several recording sensor placements prove that few-shot learning is a reliable technique even in real-world scenarios and gives valuable insights for developing an in-car emergency vehicle detection system.

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Section: Related Workmentioning

confidence: 99%

Few-Shot Emergency Siren Detection

Cantarini

Gabrielli

Squartini

2022

Sensors

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“…information extraction [211], matchine translation [100], charge prediction [178], sequence labeling [349] Audio&Speech audio/speech/sound classification [350], [351], [352], [353], [354], [355], text-to-speech [356], [357], [358], [359], acoustic/sound event detection [360], [361], [362], speech generation [350], [363], keyword/command recognition [364], keyword spotting [365], human-fall detection [366], speaker recognition [367],…”

Section: Applicationsmentioning

confidence: 99%

Learning from Very Few Samples: A Survey

Lu¹,

Gong²,

Ye³

et al. 2020

Preprint

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Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.

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

Cited by 2 publications

References 0 publications

Few-Shot Emergency Siren Detection

Few-Shot Emergency Siren Detection

Learning from Very Few Samples: A Survey

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