Open-set Evolving Acoustic Scene Classification System

Saki, Fatemeh; Guo, Yinyi; Hung, Cheng‐Yu; Kim, Lae-hoon; Deshpande, M. R.; Moon, Sunkuk; Koh, Eunjeong; Visser, Erik

doi:10.33682/en2t-9m14

Cited by 7 publications

(3 citation statements)

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“…Ref 31 applied the CNN architecture for closeset classi cation set, and other deep convolutional autoencoders (DCAES) for unknown detection. Ref 32 utilizes the support vector data description (SVDD) model to construct the effective description of the known data boundary in the feature space, and rejects the out-of-distribution samples. Ref 33 adopted class-conditioned autoencoder to detect the unknown, according to the assumption that the unknown has larger reconstruction errors than the known samples.…”

Section: Open Set Recognitionmentioning

confidence: 99%

Open Set Classification of Sound Event

Jie

Lee

2023

Preprint

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Sound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip that is obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification address involves distinguishing what kind of audio clip it is from the given audio sequence. Nowadays, it is a common issue to solve in the following pipeline: audio pre-processing◊perceptual feature extraction◊classification algorithm. In this paper, we improve the traditional sound event classification algorithm to identify unknown sound events by using the deep learning method. The compact cluster structure in the feature space for known classes is helpful in recognizing unknown classes by allowing large room to locate unknown samples in the embedded feature space. Based on this concept: we applied center loss and supervised contrastive loss to optimize the model. The center loss tries to minimize the intra class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class features from one another. In addition, we explored the performance of self-supervised learning in detecting unknown sound events. The experimental results demonstrate that our proposed open set sound event classification algorithm and self-supervised learning approach achieve sustained performance improvements in various datasets.

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Section: Open Set Recognitionmentioning

confidence: 99%

Open Set Classification of Sound Event

Jie

Lee

2023

Preprint

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“…This scenario was first addressed as part of the DCASE 2019 challenge in Task 1C "Open-set Acoustic Scene Classification" [66]. [67]. Unknown samples are first rejected by a recognition model before the algorithm tries to identify underlying (hidden) classes in these samples in an unsupervised manner.…”

Section: Closed/open Set Classificationmentioning

confidence: 99%

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Abeßer

2020

Applied Sciences

129

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The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.

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“…Recently, in the areas of computer vision and natural language processing [6,7], Transfer Learning (TL) has shown great potential to 1) identify the transferable knowledge by accommodating new knowledge and 2) retain previously learned information. Some recent works have explored TL for audio applications [8,9,10,11], which focus on knowledge transfer between databases with various qualities, mismatch downstream tasks, and domains. However, it remains to be seen how a flexible TL model for SED task to audio knowledge transfer can be done.…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning Algorithm For Sound Event Detection

Koh

Saki

Guo

et al. 2020

2020 IEEE International Conference on Multimedia and Expo (ICME)

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This paper presents a new learning strategy for the Sound Event Detection (SED) system to tackle the issues of i) knowledge migration from a pre-trained model to a new target model and ii) learning new sound events without forgetting the previously learned ones without re-training from scratch. In order to migrate the previously learned knowledge from the source model to the target one, a neural adapter is employed on the top of the source model. The source model and the target model are merged via this neural adapter layer. The neural adapter layer facilitates the target model to learn new sound events with minimal training data and maintaining the performance of the previously learned sound events similar to the source model. Our extensive analysis on the DCASE16 and US-SED dataset reveals the effectiveness of the proposed method in transferring knowledge between source and target models without introducing any performance degradation on the previously learned sound events while obtaining a competitive detection performance on the newly learned sound events.

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Open-set Evolving Acoustic Scene Classification System

Cited by 7 publications

References 0 publications

Open Set Classification of Sound Event

Open Set Classification of Sound Event

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Incremental Learning Algorithm For Sound Event Detection

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