Human Sound Classification based on Feature Fusion Method with Air and Bone Conducted Signal

Li, Xu; Wang, Jing; Wang, Lizhong; Bi, Sijun; Zhang, Jianqian; Ma, Qiang

doi:10.21437/interspeech.2022-348

Cited by 2 publications

(2 citation statements)

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“…By relying on a direct coupling to the wearer's head, they are capable of capturing speech with a much higher level of isolation at the cost of somewhat unnatural sounding recordings [34]. Using this unique property, they have already been employed in applications such as audio enhancement by fusion with a standard air conduction signal [43], human sound classification [45], and pitch detection [33]. While they require direct physical contact to be effective, the ear has been shown to be an effective site for bone conduction microphone placement [39], making them a natural fit for integration in tiny earbuds.…”

Section: Bone Conduction Microphonesmentioning

confidence: 99%

In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms

Schilk

Polvani

Ronco

et al. 2023

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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Section: Bone Conduction Microphonesmentioning

confidence: 99%

In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms

Schilk

Polvani

Ronco

et al. 2023

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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“…Most of the systems apply data augmentation and transfer learning techniques. Some other systems propose the fusion of different classifiers [28][29][30][31] and features [32,33].…”

Section: Introductionmentioning

confidence: 99%

NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications

Diez,

Saratxaga,

Salegi

et al. 2023

Applied Sciences

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The use of continuous monitoring systems to control aspects such as noise pollution has grown in recent years. The commercial monitoring systems used to date only provide information on noise levels but do not identify the noise sources that generate them. The identification of noise sources is an important aspect in order to apply corrective measures to mitigate the noise levels. In this sense, new technological advances like machine listening can enable the addition of other capabilities to sound monitoring systems such as the detection and classification of noise sources. Despite the increasing development of these systems, researchers have to face some shortcomings. The most frequent ones are on the one hand, the lack of data recorded in real environments and on the other hand, the need for automatic labelling of large volumes of data collected by working monitoring systems. In order to address these needs, in this paper, we present our own sound database recorded in an urban environment. Some baseline results for the database are provided using two original convolutional neural network based sound events classification systems. Additionally, a state of the art transformer-based audio classification system (AST) has been applied to obtain some baseline results. Furthermore, the database has been used for evaluating a semi-supervised strategy to train a classifier for automatic labelling that can be refined by human labellers afterwards.

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Human Sound Classification based on Feature Fusion Method with Air and Bone Conducted Signal

Cited by 2 publications

References 0 publications

In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms

In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms

NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications

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