Excavation equipment classification based on improved MFCC features and ELM

Cao, Jiuwen; Zhao, Tuo; Wang, Jianzhong; Wang, Ruirong; Chen, Yun

doi:10.1016/j.neucom.2016.03.113

Cited by 40 publications

(23 citation statements)

References 28 publications

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“…Related studies have successfully applied the Mel-Frequency Cepstral Coefficients (MFCCs) to speech feature extraction and object recognition, as in the literature (Nakamura et al, 2013; Luo et al, 2017; Eppe et al, 2018). The standard MFCC feature can only propose the static characteristics of the sound (Cao et al, 2017). In order to better reflect the dynamic characteristics of the sound, this paper uses the first-order and second-order different features of the static 12-order MFCCs to obtain the dynamic features of 36-dimensional MFCCs.…”

Section: Acoustic Dataset Collectionmentioning

confidence: 99%

Open-Environment Robotic Acoustic Perception for Object Recognition

et al. 2019

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Object recognition in containers is extremely difficult for robots. Dynamic audio signals are more responsive to an object's internal property. Therefore, we adopt the dynamic contact method to collect acoustic signals in the container and recognize objects in containers. Traditional machine learning is to recognize objects in a closed environment, which is not in line with practical applications. In real life, exploring objects is dynamically changing, so it is necessary to develop methods that can recognize all classes of objects in an open environment. A framework for recognizing objects in containers using acoustic signals in an open environment is proposed, and then the kernel k nearest neighbor algorithm in an open environment (OSKKNN) is set. An acoustic dataset is collected, and the feasibility of the method is verified on the dataset, which greatly promotes the recognition of objects in an open environment. And it also proves that the use of acoustic to recognize objects in containers has good value.

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Section: Acoustic Dataset Collectionmentioning

confidence: 99%

Open-Environment Robotic Acoustic Perception for Object Recognition

et al. 2019

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“…To obtain the spectrum of the sound signal, the FFT is conducted on the short time frame sound signal x (n) , and the equation (Cao, Zhao, & Wang, 2017) is…”

Section: Static Characteristic Parameter Extractionmentioning

confidence: 99%

“…(2) the second difference of MFCC The equation of the second difference of MFCC is as follows (Cao et al, 2017):…”

Section: Dynamic Characteristic Parameter Extractionmentioning

confidence: 99%

Wood material recognition for industrial applications

Liu

Deng

et al. 2018

Systems Science & Control Engineering

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Material recognition is an essential problem in the industrial automation community. In this study, we develop a machine learning method to identify the wood material. We extract various feature descriptors for the sound signal and perform a comprehensive comparison with the common classifier. The experimental validations achieve the best feature combination. Besides, we developed a practical stylus to collect sound signal from the wooden samples and present promising preliminary results.

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“…Among the cepstrum based methods, cepstral coefficient methods are the most prominent ones, which represent audio based on perception of human auditory systems [7]. It not only has excellent performance in the field of speech processing such as speaker recognition [8], music style recognition [9] and language recognition [10], but also has been successfully applied to construction equipment recognition [11], rock burst sound signal recognition [12] and other engineering fields. Mei et al [13] used cepstrum coefficient and analyzed signals of bridge deformation, midspan stiffness etc.…”

Section: Introductionmentioning

confidence: 99%

A novel acoustic emission parameter for predicting rock failure during Brazilian test based on cepstrum analysis

Wang

Song

et al. 2020

E3S Web Conf.

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Acoustic emission (AE) is widely used in the monitoring of coal and rock stability and early warning of dynamic disasters in mines. In this work, the Brazilian split test was carried out on limestone samples along with collecting full waveform AE signal during the entire loading process. The linear cepstrum coefficient (LCC) was used as the characteristic parameter of AE to analyze the correlation between the LCC of AE and the load of the samples. The results show that the LCC is an effective AE characteristic parameter. The first to fourth parameters of the LCC calculated using 1 s AE waveform meet the linear change relationship with the load of the samples. The correlation coefficients are 0.969, 0.943, 0.925, 0.833, respectively. The LCC of AE proposed in this work can be used as a characteristic parameter for predicting the tensile failure of rocks, which is helpful to improve the application effect of AE in the monitoring of coal and rock dynamic disasters and the stability of high and steep slopes.

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Excavation equipment classification based on improved MFCC features and ELM

Cited by 40 publications

References 28 publications

Open-Environment Robotic Acoustic Perception for Object Recognition

Open-Environment Robotic Acoustic Perception for Object Recognition

Wood material recognition for industrial applications

A novel acoustic emission parameter for predicting rock failure during Brazilian test based on cepstrum analysis

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