scholarly journals Automatic Segmentation of Ethnomusicological Field Recordings

2019 ◽  
Vol 9 (3) ◽  
pp. 439
Author(s):  
Matija Marolt ◽  
Ciril Bohak ◽  
Alenka Kavčič ◽  
Matevž Pesek
Keyword(s):  
Folk Music ◽  
Classification Accuracy ◽  
Background Noise ◽  
Network Architecture ◽  
Automatic Segmentation ◽  
Probabilistic Approach ◽  
Segmentation Method ◽  
Field Recordings ◽  
Deep Networks ◽  
Feature Based

The article presents a method for segmentation of ethnomusicological field recordings. Field recordings are integral documents of folk music performances captured in the field, and typically contain performances, intertwined with interviews and commentaries. As these are live recordings, captured in non-ideal conditions, they usually contain significant background noise. We present a segmentation method that segments field recordings into individual units labelled as speech, solo singing, choir singing, and instrumentals. Classification is based on convolutional deep networks, and is augmented with a probabilistic approach for segmentation. We describe the dataset gathered for the task and the tools developed for gathering the reference annotations. We outline a deep network architecture based on residual modules for labelling short audio segments and compare it to the more standard feature based approaches, where an improvement in classification accuracy of over 10% was obtained. We also present the SeFiRe segmentation tool that incorporates the presented segmentation method.

scholarly journals Detection and Classification of Leukemia using MPFCM Segmentation and Random Forest with Boosting Techniques

2018 ◽  
Vol 13 (1) ◽  
pp. 5933-5939
Author(s):  
Kalaiselvi Chinnathambi
Keyword(s):  
Blood Cell ◽  
Classification Accuracy ◽  
Visual Inspection ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Blood Disorders ◽  
Adaptive Boosting ◽  
Blast Cells ◽  
Fuzzy C Means Algorithm

Identification of blood disorders is through visual inspection of microscopic blood cell images. From the identification of blood disorders lead to classification of certain diseases related to blood. We propose an automatic segmentation method for segmenting White blood cell images. Firstly, modified possibilistic fuzzy c-means algorithm is proposed to detect the contours in the image. The GLCM features are extracted and features are selected by MRMR. Adaptive boosting and LS Boosting has been utilized to classify blast cells from normal lymphocyte cells. Comparison performance of classification accuracy was carried out. The effectiveness of the classification system is tested with the total of 80 samples collected. The evaluated results demonstrate that our method outperformed the existing systems with an accuracy of 88  %.

A New Image Segmentation Method Based on Three-Dimensional Neural Network

2012 ◽  
Vol 490-495 ◽  
pp. 157-161
Author(s):  
Guo Fu Lin
Keyword(s):  
Image Segmentation ◽  
Classification Accuracy ◽  
Probabilistic Approach ◽  
Three Dimensional ◽  
Training Method ◽  
Segmentation Method ◽  
Brain Images ◽  
Feature Sets ◽  
Mr Brain Images ◽  
Mr Brain

In this paper, a three-dimensional probabilistic approach for MR brain image segmentation is proposed. Based on the noise-free representative reference vectors provided by SOM, the results of the 3D-PNN method are superior to other traditional algorithms. In addition to the 3D-PNN architecture, a fast three-step training method is proposed. The proposed approach also incorporates structure tensor to find appropriate feature sets for the 3D-PNN with respect to resulting classification accuracy. Computational results with simulated MR brain images have shown the promising performance of the proposed method.

scholarly journals Probabilistic Segmentation of Folk Music Recordings

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ciril Bohak ◽  
Matija Marolt
Keyword(s):  
Folk Music ◽  
Distance Measure ◽  
Automatic Segmentation ◽  
Segment Length ◽  
Programming Approach ◽  
Commercial Music ◽  
Dynamic Programming Approach ◽  
Field Recordings ◽  
Dynamic Time ◽  
Music Recordings

The paper presents a novel method for automatic segmentation of folk music field recordings. The method is based on a distance measure that uses dynamic time warping to cope with tempo variations and a dynamic programming approach to handle pitch drifting for finding similarities and estimating the length of repeating segment. A probabilistic framework based on HMM is used to find segment boundaries, searching for optimal match between the expected segment length, between-segment similarities, and likely locations of segment beginnings. Evaluation of several current state-of-the-art approaches for segmentation of commercial music is presented and their weaknesses when dealing with folk music are exposed, such as intolerance to pitch drift and variable tempo. The proposed method is evaluated and its performance analyzed on a collection of 206 folk songs of different ensemble types: solo, two- and three-voiced, choir, instrumental, and instrumental with singing. It outperforms current commercial music segmentation methods for noninstrumental music and is on a par with the best for instrumental recordings. The method is also comparable to a more specialized method for segmentation of solo singing folk music recordings.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

scholarly journals Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

2021 ◽  
Author(s):  
Sebastian Nowak ◽  
Narine Mesropyan ◽  
Anton Faron ◽  
Wolfgang Block ◽  
Martin Reuter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Liver Cirrhosis ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Automatic Segmentation ◽  
Analysis Data ◽  
Caudate Lobe ◽  
Natural Image ◽  
Liver Segmentation

Abstract Objectives To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. Methods The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. Results Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). Conclusion This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. Key Points • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi202-vi203
Author(s):  
Alvaro Sandino ◽  
Ruchika Verma ◽  
Yijiang Chen ◽  
David Becerra ◽  
Eduardo Romero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Vessels ◽  
Network Architecture ◽  
Automatic Segmentation ◽  
Area Under The Curve ◽  
Primary Treatment ◽  
Microvascular Proliferation ◽  
Vascular Proliferation ◽  
Testing Accuracy ◽  
Future Work

Abstract PURPOSE Glioblastoma is a highly heterogeneous brain tumor. Primary treatment for glioblastoma involves maximally-safe surgical resection. After surgery, resected tissue slides are visually analyzed by neuro-pathologists to identify distinct histological hallmarks characterizing glioblastoma including high cellularity, necrosis, and vascular proliferation. In this work, we present a hierarchical deep learning-based strategy to automatically segment distinct Glioblastoma niches including necrosis, cellular tumor, and hyperplastic blood vessels, on digitized histopathology slides. METHODS We employed the IvyGap cohort for which Hematoxylin and eosin (H&E) slides (digitized at 20X magnification) from n=41 glioblastoma patients were available. Additionally, expert-driven segmentations of cellular tumor, necrosis, and hyperplastic blood vessels (along with other histological attributes) were made available. We randomly employed n=120 slides from 29 patients for training, n=38 slides from 6 cases for validation, and n=30 slides from 6 patients to feed our deep learning model based on Residual Network architecture (ResNet-50). ~2,000 patches of 224x224 pixels were sampled for every slide. Our hierarchical model included first segmenting necrosis from non-necrotic (i.e. cellular tumor) regions, and then from the regions segmented as non-necrotic, identifying hyperplastic blood-vessels from the rest of the cellular tumor. RESULTS Our model achieved a training accuracy of 94%, and a testing accuracy of 88% with an area under the curve (AUC) of 92% in distinguishing necrosis from non-necrotic (i.e. cellular tumor) regions. Similarly, we obtained a training accuracy of 78%, and a testing accuracy of 87% (with an AUC of 94%) in identifying hyperplastic blood vessels from the rest of the cellular tumor. CONCLUSION We developed a reliable hierarchical segmentation model for automatic segmentation of necrotic, cellular tumor, and hyperplastic blood vessels on digitized H&E-stained Glioblastoma tissue images. Future work will involve extension of our model for segmentation of pseudopalisading patterns and microvascular proliferation.

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

2021 ◽  
Author(s):  
Haruka Kamachi ◽  
Takumi Kondo ◽  
Tahera Hossain ◽  
Anna Yokokubo ◽  
Guillaume Lopez
Keyword(s):  
Automatic Segmentation ◽  
Bone Conduction ◽  
Segmentation Method

scholarly journals An automatic segmentation method for heart sounds

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Qingshu Liu ◽  
Xiaomei Wu ◽  
Xiaojing Ma
Keyword(s):  
Automatic Segmentation ◽  
Heart Sounds ◽  
Segmentation Method
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