Sparse feature learning for instrument identification: Effects of sampling and pooling methods

Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

doi:10.1121/1.4946988

Cited by 17 publications

(11 citation statements)

References 2 publications

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“…Pooling significantly reduces the computational complexity for the processing steps. Max-pooling and average-pooling are two of the most common pooling methods across various tasks [ 40 ]. In this research, max-pooling is chosen for the resolution reduction.…”

Section: Learning Methodsmentioning

confidence: 99%

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

et al. 2017

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As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features.

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Section: Learning Methodsmentioning

confidence: 99%

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

et al. 2017

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“…Pooling operations significantly reduce the computational complexity. Max-pooling and average-pooling are the two most common pooling methods across various tasks [35]. In this research, max-pooling can be expressed as…”

Section: Polling Layermentioning

confidence: 99%

Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

et al. 2018

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Machined surfaces are rough from a microscopic perspective no matter how finely they are finished. Surface roughness is an important factor to consider during production quality control. Using modern techniques, surface roughness measurements are beneficial for improving machining quality. With optical imaging of machined surfaces as input, a convolutional neural network (CNN) can be utilized as an effective way to characterize hierarchical features without prior knowledge. In this paper, a novel method based on CNN is proposed for making intelligent surface roughness identifications. The technical scheme incorporates there elements: texture skew correction, image filtering, and intelligent neural network learning. Firstly, a texture skew correction algorithm, based on an improved Sobel operator and Hough transform, is applied such that surface texture directions can be adjusted. Secondly, two-dimensional (2D) dual tree complex wavelet transform (DTCWT) is employed to retrieve surface topology information, which is more effective for feature classifications. In addition, residual network (ResNet) is utilized to ensure automatic recognition of the filtered texture features. The proposed method has verified its feasibility as well as its effectiveness in actual surface roughness estimation experiments using the material of spheroidal graphite cast iron 500-7 in an agricultural machinery manufacturing company. Testing results demonstrate the proposed method has achieved high-precision surface roughness estimation.

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“…A mel-scale is based on the human auditory system and is approximately logarithmic above 1 kHz [23]. We used 128 mel-frequency bins following representation learning researches on music annotation [24], [25], musical instrument identification task [26], and fingering detection of overblown flute sound [27]; this is a reasonable size that sufficiently retain the original spectral characteristics, while significantly reducing the dimensionality of the data.…”

Section: A Audio Preprocessingmentioning

confidence: 99%

Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation

Han,

Lee

2016

Preprint

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In recent years, neural network approaches have shown superior performance to conventional hand-made features in numerous application areas. In particular, convolutional neural networks (ConvNets) exploit spatially local correlations across input data to improve the performance of audio processing tasks, such as speech recognition, musical chord recognition, and onset detection. Here we apply ConvNet to acoustic scene classification, and show that the error rate can be further decreased by using delta features in the frequency domain. We propose a multiplewidth frequency-delta (MWFD) data augmentation method that uses static mel-spectrogram and frequency-delta features as individual input examples. In addition, we describe a ConvNet output aggregation method designed for MWFD augmentation, folded mean aggregation, which combines output probabilities of static and MWFD features from the same analysis window using multiplication first, rather than taking an average of all output probabilities. We describe calculation results using the DCASE 2016 challenge dataset, which shows that ConvNet outperforms both of the baseline system with hand-crafted features and a deep neural network approach by around 7%. The performance was further improved (by 5.7%) using the MWFD augmentation together with folded mean aggregation. The system exhibited a classification accuracy of 0.831 when classifying 15 acoustic scenes.

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Sparse feature learning for instrument identification: Effects of sampling and pooling methods

Cited by 17 publications

References 2 publications

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation

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