Towards joint sound scene and polyphonic sound event recognition

Bear, Helen L.; Nolasco, Ines; Benetos, Emmanouil

doi:10.48550/arxiv.1904.10408

Cited by 2 publications

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“…Recently there have been few studies to jointly conduct the ASC and the audio tagging task [10][11][12]. Bear et.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Scene Classification using Audio Tagging

Jung¹,

Shim²,

Kim³

et al. 2020

Preprint

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Acoustic scene classification systems using deep neural networks classify given recordings into pre-defined classes. In this study, we propose a novel scheme for acoustic scene classification which adopts an audio tagging system inspired by the human perception mechanism. When humans identify an acoustic scene, the existence of different sound events provides discriminative information which affects the judgement. The proposed framework mimics this mechanism using various approaches. Firstly, we employ three methods to concatenate tag vectors extracted using an audio tagging system with an intermediate hidden layer of an acoustic scene classification system. We also explore the multi-head attention on the feature map of an acoustic scene classification system using tag vectors. Experiments conducted on the detection and classification of acoustic scenes and events 2019 task 1-a dataset demonstrate the effectiveness of the proposed scheme. Concatenation and multi-head attention show a classification accuracy of 75.66 % and 75.58 %, respectively, compared to 73.63 % accuracy of the baseline. The system with the proposed two approaches combined demonstrates an accuracy of 76.75 %.

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“…Recently there have been few studies to jointly conduct the ASC and the audio tagging task [10][11][12]. Bear et.…”

Section: Introductionmentioning

confidence: 99%

“…al. [10] proposed to jointly conduct sound event detection and ASC task where ASC is conducted by a majority voting mechanism based on the result of sound event detection. Imoto et.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Scene Classification using Audio Tagging

Jung¹,

Shim²,

Kim³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Hajihashemi,

Alavigharahbagh,

Machado

et al. 2024

Multimed Tools Appl

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The detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the system’s efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.

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Towards joint sound scene and polyphonic sound event recognition

Cited by 2 publications

References 0 publications

Acoustic Scene Classification using Audio Tagging

Acoustic Scene Classification using Audio Tagging

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

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