Acoustic Scene Classification Using Audio Tagging

Jung, Jee-weon; Shim, Hye-jin; Kim, Ju-Ho; Kim, Seung-bin; Yu, Ha-Jin

doi:10.21437/interspeech.2020-0992

Cited by 4 publications

(10 citation statements)

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“…Scarce research on the combination of related sound classification tasks has been conducted [10,11,13,14]. Imoto et al [13] assumed that ASC and SED are related and performed them simultaneously using a multitask learning framework.…”

Section: Related Workmentioning

confidence: 99%

“…Imoto et al [13] assumed that ASC and SED are related and performed them simultaneously using a multitask learning framework. Jung et al [10] aimed to mimic the human perception mechanism of leveraging TAG for ASC by applying an attention mechanism using the output of a pretrained TAG network. However, we assume that analysis and architecture designs accounting for the task relations can further improve integrated systems.…”

Section: Related Workmentioning

confidence: 99%

“…1-(a) and -(b)) is inspired by human cognitive processes. The first architecture resembles the perception mechanism of an adult, expanding the method in [10]. This architecture first performs the event detection (i.e., SED) using a convolutional recurrent neural network (CRNN) to then perform segment-level scene classification (i.e., ASC).…”

Section: Integrated Frameworkmentioning

confidence: 99%

“…Few studies have explored DNN architectures that use two tasks. Jung et al [10,11] applied an attention mechanism for ASC using a pretrained TAG system. The study was inspired by human cognition (e.g., [12]), as humans first perform SED and leverage this information to classify scenes.…”

Section: Introductionmentioning

confidence: 99%

“…The integrated framework performs two segment-level tasks and one frame-level task using a single DNN that jointly learns and performs ASC, TAG, and SED (three tasks are introduced in details in Section 2). The first architecture resembles the short-term perception of adults for ASC, being similar to the method used by Jung et al [10]. Instead of using a TAG system to improve an ASC system, we propose an integrated framework that first performs SED followed by ASC and TAG.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

DCASENET: An Integrated Pretrained Deep Neural Network for Detecting and Classifying Acoustic Scenes and Events

Jung

Shim

Kim

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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Although acoustic scenes and events include many related tasks, their combined detection and classification have been scarcely investigated. We propose three architectures of deep neural networks that are integrated to simultaneously perform acoustic scene classification, audio tagging, and sound event detection. The first two architectures are inspired by human cognitive processes. The first architecture resembles the short-term perception for scene classification of adults, who can detect various sound events that are then used to identify the acoustic scene. The second architecture resembles the long-term learning of babies, being also the concept underlying self-supervised learning. Babies first observe the effects of abstract notions such as gravity and then learn specific tasks using such perceptions. The third architecture adds a few layers to the second one that solely perform a single task before its corresponding output layer. The aim is to build an integrated system that can serve as a pretrained model to perform the three abovementioned tasks. Experiments on three datasets demonstrate that the proposed architecture, called DcaseNet, can be either directly used for any of the tasks while providing suitable results or fine-tuned to improve the performance of one task. The code and pretrained DcaseNet weights are available at https://github.com/Jungjee/DcaseNet.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Integrated Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

DCASENET: An Integrated Pretrained Deep Neural Network for Detecting and Classifying Acoustic Scenes and Events

Jung

Shim

Kim

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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Shallow Convolution-Augmented Transformer with Differentiable Neural Computer for Low-Complexity Classification of Variable-Length Acoustic Scene

Seo¹,

Lee²,

Kim³

2021

Interspeech 2021

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Attentive Max Feature Map for Acoustic Scene Classification with Joint Learning considering the Abstraction of Classes

Shim¹,

Kim²,

Jung³

et al. 2021

Preprint

Self Cite

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The attention mechanism has been widely adopted in acoustic scene classification. However, we find that during the process of attention exclusively emphasizing information, it tends to excessively discard information although improving the performance. We propose a mechanism referred to as the attentive max feature map which combines two effective techniques, attention and max feature map, to further elaborate the attention mechanism and mitigate the abovementioned phenomenon. Furthermore, we explore various joint learning methods that utilize additional labels originally generated for subtask B (3-classes) on top of existing labels for subtask A (10-classes) of the DCASE2020 challenge. We expect that using two kinds of labels simultaneously would be helpful because the labels of the two subtasks differ in their degree of abstraction. Applying two proposed techniques, our proposed system achieves state-of-the-art performance among single systems on subtask A. In addition, because the model has a complexity comparable to subtask B's requirement, it shows the possibility of developing a system that fulfills the requirements of both subtasks; generalization on multiple devices and low-complexity.

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Acoustic Scene Classification Using Audio Tagging

Cited by 4 publications

References 0 publications

DCASENET: An Integrated Pretrained Deep Neural Network for Detecting and Classifying Acoustic Scenes and Events

DCASENET: An Integrated Pretrained Deep Neural Network for Detecting and Classifying Acoustic Scenes and Events

Shallow Convolution-Augmented Transformer with Differentiable Neural Computer for Low-Complexity Classification of Variable-Length Acoustic Scene

Attentive Max Feature Map for Acoustic Scene Classification with Joint Learning considering the Abstraction of Classes

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