Acoustic feature extraction by statistics based local binary pattern for environmental sound classification

Kobayashi, Takumi; Ye, Jiaxing

doi:10.1109/icassp.2014.6854161

Cited by 41 publications

(27 citation statements)

References 17 publications

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“…2) Histogram of oriented Gradients (HoG) local descriptor: Latest research toward ASC manifests that 2-dimensional local descriptors are efficient for describing environmental sounds, such as using local Binary patterns (LBP) [15] and histograms of oriented gradients (HoG) [16]. In a similar vein, we adopt HoG descriptor to characterize spectro-temporal structures in acoustic scenes.…”

Section: Proposed Methodsmentioning

confidence: 99%

Discriminant kernel learning for acoustic scene classification with multiple observations

Kobayashi

Tsuda

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose a novel kernel learning scheme for acoustic scene classification using multiple short-term observations. The method takes inspiration from the recent result of psychological research -"Humans use summary statistics to perceive auditory sequences"; we endeavor to devise computational framework imitating such important auditory mechanism for acoustic scene parsing. Conventional schemes usually encode spectro-temporal patterns with a compact feature vector by timeaveraging, e.g. in Gaussian Mixture models (GMM). However, such integration may not be the ideal, since the arithmetic mean is vulnerable to extreme outliers which can be generated by sounds irrelevant to scene category. In this work, an effective scheme has been developed to exploit rich discriminant information from multiple short-term observations of an acoustic scene. Concretely, we first segment audio recording into short slices, e.g. 2 seconds; one vector can be extracted from each slice consisting of descriptive features. Then, we employ the resultant feature matrix to represent an acoustic scene. Since discriminant information of an acoustic scene can be characterized by either global structure or local patterns, we perform heterogeneous kernel analysis in hybrid feature spaces. Moreover, we conditionally fuse the twoway discriminant information to achieve better classification. The proposed method is validated using DCASE2016 challenge dataset. Experimental results demonstrated the effectiveness of our approach.

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Section: Proposed Methodsmentioning

confidence: 99%

Discriminant kernel learning for acoustic scene classification with multiple observations

Kobayashi

Tsuda

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…While various types of image feature have been proposed so far [4,14,19,37] and extended for motion features [10,15,33], local binary pattern (LBP) [26,31] is one of the commonly used features due to its simple formulation and high performance. The LBP method has been mainly applied to measure texture characteristics [7,8,[26][27][28], and in recent years it is shown to be favorably applicable to various kinds of visual recognition tasks besides texture classification, such as face recognition [1,34], face detection [9], pedestrian detection [37] and sound classification [16]. LBP is also known as census transform [40] and utilized for a holistic image descriptor [38].…”

Section: Introductionmentioning

confidence: 99%

“…It is also possible to build noise-robust LBP by simply considering local statistics, mean [9] and median [8], as a threshold instead of the center pixel intensity in coding. To further improve robustness, we have recently extended LBP to fully incorporate the statistical information, mean and variance, in the processes both of coding and weighting [16]. For more elaborated review of LBP, refer to [31].…”

Section: Introductionmentioning

confidence: 99%

“…We first generalize the LBP formulation by focusing on the two fundamental processes of coding and weighting, and then along the line of [7][8][9]16], propose discriminative LBP by providing a discriminative approach to determine those two ingredients. In the discriminative approach, LBP coding is regarded as separating local pixel intensity distribution into two modes (clusters) and from that viewpoint, a threshold is optimized by maximizing the Fisher discriminant score which is further utilized in weighting.…”

Section: Introductionmentioning

confidence: 99%

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Discriminative local binary pattern

Kobayashi

2016

Machine Vision and Applications

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Local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective features with a low computational cost. There, however, are some limitations mainly regarding sensitivity to noise and loss of image contrast information. In this paper, we propose a novel LBPbased feature extraction method to remedy those drawbacks without degrading the simplicity of the original LBP formulation. LBP is built upon encoding local pixel intensities into binary patterns which can be regarded as separating them into two modes (clusters). We introduce Fisher discriminant criterion to optimize the LBP coding for exploiting binary patterns more stably and discriminatively with robustness to noise. Besides, image contrast information is incorporated in a unified way by leveraging the discriminant score as a weight on the binary pattern; therefore, the prominent patterns, such as around edges, are emphasized. The proposed method is applicable to extract not only image features but also motion features by both efficiently decomposing a XYT volume patch into 2-D patches and employing the effective thresholding strategy based on the volume patch. In the experiments on various visual recognition tasks, the proposed method exhibits superior performance compared to the ordinary LBP and the other methods.

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“…Thus, an automatic identification and location technology of self-shattered glass insulator is presented, by which the self-shattered insulators can be accurately identified and located by extracting its local binary pattern (LBP) feature. [11][12][13] 2 Framework of Automatic Identification and Localization Technology An automatic identification and localization technology of self-shattered glass insulators consists of three parts: cameras, which can be installed on towers or UAV, the 4G/OPGW communication network, and the monitoring center, where the identification and localization algorithm is embedded into the expert software, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification and location technology of glass insulator self-shattering

Huang

Zhang

2017

J. Electron. Imaging

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Abstract. The insulator of transmission lines is one of the most important infrastructures, which is vital to ensure the safe operation of transmission lines under complex and harsh operating conditions. The glass insulator often self-shatters but the available identification methods are inefficient and unreliable. Then, an automatic identification and localization technology of self-shattered glass insulators is proposed, which consists of the cameras installed on the tower video monitoring devices or the unmanned aerial vehicles, the 4G/OPGW network, and the monitoring center, where the identification and localization algorithm is embedded into the expert software. First, the images of insulators are captured by cameras, which are processed to identify the region of insulator string by the presented identification algorithm of insulator string. Second, according to the characteristics of the insulator string image, a mathematical model of the insulator string is established to estimate the direction and the length of the sliding blocks. Third, local binary pattern histograms of the template and the sliding block are extracted, by which the self-shattered insulator can be recognized and located. Finally, a series of experiments is fulfilled to verify the effectiveness of the algorithm. For single insulator images, Ac, Pr , and Rc of the algorithm are 94.5%, 92.38%, and 96.78%, respectively. For double insulator images, Ac, Pr , and Rc are 90.00%, 86.36%, and 93.23%, respectively.

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Acoustic feature extraction by statistics based local binary pattern for environmental sound classification

Cited by 41 publications

References 17 publications

Discriminant kernel learning for acoustic scene classification with multiple observations

Discriminant kernel learning for acoustic scene classification with multiple observations

Discriminative local binary pattern

Automatic identification and location technology of glass insulator self-shattering

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