Journal of Measurements in Engineering

Automatic transcription of sound signals can convert audio to musical notes, which has significant research value. This paper extracted dual-channel constant Q transform (CQT) spectra from piano audio as features. In the design of the automatic transcription model, a CNN was employed to extract local features and then combined with a Transformer model to obtain global features. A CNN-Transformer automatic transcription model was established using a two-layer CNN and three-layer Transformers. Experiments were conducted on the MAPS and MAESTRO datasets. The results showed that dual-channel CQT outperformed short-time Fourier transform (STFT) and mono CQT in auto-transcription. Dual-channel CQT achieved the best results on frame-level transcription for the MAPS dataset, with a P value of 0.9115, an R value of 0.8055, and an F1 value of 0.8551. A sliding window with seven frames yielded the best transcription results. Compared with the deep neural network and CNN models, the CNN-Transformer model demonstrated superior performance, achieving an F1 value of 0.8551 and 0.9042 at the frame level for MAPS and MAESTRO datasets, respectively. These findings confirm the designed model's reliability for automatic piano audio transcription and highlight its practical applicability.

Peng Wang

Ning Dai

Processing piano audio: research on an automatic transcription model for sound signals

钢琴音频处理：声音信号自动转录模型的研究

Hyperspectral image (HSI) feature extraction is an important means to improve the classification of different ground features. According to the structural characteristics of hyperspectral data, the general feature extraction scheme can extract features from the point of view of spectral dimension, spatial and spatial spectrum. And the feature extraction time is also an index to measure the feature extraction method. Therefore, from the perspective of spatial dimension, this paper explores the relationship between HSI feature extraction time and training sample ratio. Three groups of HSIs sets were used for correlation test and analysis in the experiment. According to the characteristics of different data sets, the best selection scheme between spatial domain feature extraction method and training samples is given.

Jian Tang

Dan Li

Hongbing Liu

Xiaochun Liu

Dan Luo

Hong Zhou

Hongyan Cui

Qianliang Xiao

Research on the relationship between feature extraction time and training samples of hyperspectral image based on spatial domain

基于空间域的高光谱图像特征提取时间与训练样本关系研究

The application of cavitation water jet technology to modify medical implant surfaces facilitates the formation of distinctive microporous structures, thereby enhancing the contact area between the implant and alveolar bone, and improving osseointegration. Therefore, the microstructure characteristics of the modified implant are one of the important evaluation indicators of the modification effect. This paper proposes a processing method for the identification and statistical analysis of surface micro-morphology images. The method incorporates techniques such as image enhancement, image segmentation, morphological image processing methods, and pixel matrix operations, enabling automated quantification of pit counts, the relative positions of the pits, and other topographic characteristics of the material surface. Simultaneously, the microstructure of each pit is spatially fitted and reconstructed to standardize measurement benchmarks for pit diameter and depth characteristics. This facilitates in-depth multi-dimensional analysis of material surface characteristic information and provides foundational support for further exploration of cavitation jet modification technology. In the study, the modification effect of processing time on the surface morphology of titanium metal was used as an application case. A surface morphology feature information database was established under different processing times, and statistical analysis was conducted on proportion, structural distribution, and other characteristics in the focus areas. The results show that the diameter and proportion distribution of the pits produced by cavitation jet modification tend to be stable when the jet pressure and standoff distance remain constant, while the depth of the pit increases with the increasing processing time.

Lianxu Zhang

Wenqi Ma

Hongyi Sun

Wenhao Dai

Identification and characteristic statistics of surface microstructure of titanium metal based on cavitation water jet

基于空化水射流的钛金属表面微观结构识别与特征统计

Microstructure modifications on thin photoelectrical properties refers to changes in the microscopic structure of thin films that affect their ability to convert light into electricity. This study investigates the deposition and post-treatment effects in the electric and visual qualities of GZO, IZO, and ZnO thin layers, aiming to enhance their applicability in electronic and optoelectronic devices, according to the X-raydiffraction (XRD) examination. Thin films were deposited on glass substrates using magnetic sparking at thicknesses of 300 nm and 500 nm, followed by treatments like wet aging and annealing at 220 °C. The results showed significant improvements in crystallinity and optical characteristics, with ZnO films exhibiting a preferred (003) orientation. IZO films demonstrated notable mobility at 10.96 cm 2 /V-sec and resistivity of 2.49×10⁻ 3 ohm-cm. The novelty of this research is the novel integration of wet aging and low-temperature annealing, that notably improves thin film efficiency while maintaining structural integrity. The findings indicate that post-treatment significantly enhances the properties of these thin films, suggesting their potential for various electronic applications.

Tao Liu

Evaluating the impact of microstructure modifications on thin film photoelectric properties

评估微观结构修饰对薄膜光电性能的影响

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

Abbreviation	J. meas. eng.
Journal Impact	0.53
Quartiles(Global)	ENGINEERING, MECHANICAL(Q4)

Publication Information	Publisher: EXTRICA，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2019，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 100.00%
Average review cycle	网友分享经验：16 Weeks

Journal of Measurements in Engineering

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Share Submission Experience

Processing piano audio: research on an automatic transcription model for sound signals

Research on the relationship between feature extraction time and training samples of hyperspectral image based on spatial domain

Identification and characteristic statistics of surface microstructure of titanium metal based on cavitation water jet

Evaluating the impact of microstructure modifications on thin film photoelectric properties

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Processing piano audio: research on an automatic transcription model for sound signals

Research on the relationship between feature extraction time and training samples of hyperspectral image based on spatial domain

Identification and characteristic statistics of surface microstructure of titanium metal based on cavitation water jet

Evaluating the impact of microstructure modifications on thin film photoelectric properties

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

ISSN	2335-2124, 2424-4635
h-index	7