Environmental sound recognition: A survey

Chachada, Sachin; Kuo, C.-C. Jay

doi:10.1109/apsipa.2013.6694338

Cited by 60 publications

(14 citation statements)

References 33 publications

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“…For example, ML-based speech recognition can be used to support DHH people [26,38], and another prior study used ML to recognize environmental sound around DHH users [33]. However, despite the advances in ML-based sound recognition technologies [6,14], it is still difficult to train a single recognition model to meet all requirements from diverse users. Due to the lack of the experts of ML, there are difficulties in having the experts design a custom-made ML solution for each user.…”

Section: Background and Related Work 21 Machine Learning In Assistivmentioning

confidence: 99%

Use of Machine Learning by Non-Expert DHH People: Technological Understanding and Sound Perception

Nakao

Sugano

2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

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Recent advances in machine learning demonstrated its potential in accessibility applications. However, recognition models and their application scenarios are often defined by machine learning (ML) experts and cannot fully capture users' diverse demands with disabilities. In order to open up the full potential of ML for accessibility applications, we have to bridge the gap for non-expert people doubly caused by the technological understanding and their disabilities. In this work, we investigate how non-expert deaf and hard-of-hearing (DHH) people understand ML technologies and design ML-based sound recognition systems. We conduct a workshop study consisting of an ML lecture and an interactive learning session using a sound recognition system. Through observations during the workshop and semi-structured interviews, we clarify that non-expert DHH people start to overcome the knowledge gap. They could obtain a more detailed understanding of ML technology and how to use sounds to train ML models. CCS CONCEPTS • Social and professional topics → People with disabilities; • Computing methodologies → Machine learning.

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Section: Background and Related Work 21 Machine Learning In Assistivmentioning

confidence: 99%

Use of Machine Learning by Non-Expert DHH People: Technological Understanding and Sound Perception

Nakao

Sugano

2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

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“…In [20], the authors make an overall investigation of recognition methodologies for different categories of sounds. In [5], the authors review the current methodologies used in AESR and evaluate their performance, efficiency, and computational cost.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Concerning the general framework applied in AESR, we draw on the ideas presented in [20]. The usual pre-processing of the acoustic signal, applied in AESR includes a framing step, possibly followed by sub-framing or sequential processing.…”

Section: State-of-the-artmentioning

confidence: 99%

Environmental Acoustics Modelling Techniques for Forest Monitoring

Segarceanu¹,

Suciu²,

Gavăt³

2021

Adv. sci. technol. eng. syst. j.

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Environmental sounds detection plays an increasing role in computer science and robotics as it simulates the human faculty of hearing. It is applied in environment research, monitoring and protection, by allowing investigation of natural reserves, and showing potential risks of damage that can be deduced from the environmental acoustic. The research presented in this paper is related to the development of an intelligent forest environment monitoring solution, which applies signal analysis algorithm to detect endangering sounds. Environmental sounds are processed using some modelling algorithms based on which the acoustic forest events can be classified into one of the categories: chainsaw, vehicle, genuine forest background noise. The article will explore and compare several methodologies for environmental sound classification, among which the dominant Deep Neural Networks, the Long Short-Term Memory, and the classical Gaussian Mixtures Modelling and Dynamic Time Warping.

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“…Significant progress on environmental sound recognition was achieved when MFCC parameterization was combined with the matching pursuit (MP) algorithm [8]. MFCC has been widely used in environmental sound recognition as well as its first and second derivatives in combination with other parameterization methods [9] and [10]. Available literature studies about industrial machine sound recognition are usually based on the Morlet wavelet parameterization approach [11] to [13].…”

Section: • Optimization Of Mfcc Filter Banks For Machinery Noise • Amentioning

confidence: 99%

Automatic Recognition of Machinery Noise in the Working Environment

Lipar¹,

Čudina²,

Šteblaj³

et al. 2015

SV-JME

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A necessity for the suitable recognition of different machinery and equipment based on the sound they generate is constantly present and will increase in the future. The main motivation for the discrimination between different types of machinery sounds is to develop algorithms that can be used not only for final quality inspection but for the monitoring of the whole production line. The objective of our study is to recognize the operation of the individual machine in a production hall, where background noise level is high and constantly changing. An experimental plan was designed and performed in order to confirm the hypothesis proposing that automatic speech recognition algorithms can be applied to automatic machine recognition. The design of the automatic machine recognition procedure used in our study was divided into three stages: feature extraction, training, and recognition (classification). Additionally, a traditional mel-frequency cepstral coefficient (MFCC) procedure was adjusted for machinery noise by using different filter compositions. Finally, two classifiers were compared, the k-NN classifier and the multivariate Gaussian distribution. The results of the experiment show that machinery noise features frequency cepstral coefficients (FCC) should be extracted by using linear filter compositions and processed with recognition algorithm based on the multivariate Gaussian distribution.

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Environmental sound recognition: A survey

Cited by 60 publications

References 33 publications

Use of Machine Learning by Non-Expert DHH People: Technological Understanding and Sound Perception

Use of Machine Learning by Non-Expert DHH People: Technological Understanding and Sound Perception

Environmental Acoustics Modelling Techniques for Forest Monitoring

Automatic Recognition of Machinery Noise in the Working Environment

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