scholarly journals Time Signature Detection: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6494
Author(s):  
Jeremiah Abimbola ◽  
Daniel Kostrzewa ◽  
Pawel Kasprowski
Keyword(s):  
Deep Learning ◽  
Research Area ◽  
Future Research ◽  
Genre Classification ◽  
Signature Detection ◽  
Learning Techniques ◽  
Input Signals ◽  
Different Types ◽  
Time Signature ◽  
Music Genre Classification

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.

scholarly journals Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1863
Author(s):  
Naira Elazab ◽  
Hassan Soliman ◽  
Shaker El-Sappagh ◽  
S. M. Riazul Islam ◽  
Mohammed Elmogy
Keyword(s):  
Deep Learning ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Histological Image ◽  
Learning Techniques ◽  
Histopathology Images

Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.

scholarly journals Music Genre Classification Using Deep Learning with KNN

2021 ◽  
pp. 224-230
Author(s):  
Dr. S. Ponlatha ◽  
Mathisalini B ◽  
Deepthisri K. A ◽  
Kalaiyarasi. M ◽  
Kowshika. V
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Music Score ◽  
Genre Classification ◽  
Learning Techniques ◽  
Music Genre ◽  
The Right ◽  
Music Streaming ◽  
Music Genre Classification

Music genre is a conventional category that predicts the genre of music belonging to tradition or set of conventions. A music platform, with total assets of $26 billion, is ruling the music streaming stage today. At present, it has a huge number of tunes and it is information base and claims to have the right music score for everybody. Like, Spotify, Amazon music, Wynk has put a great deal in examination to further develop the manner in which clients find and pay attention to music. AI is at the centre of their examination. From NLP to Collaborative sifting to Deep Learning, All music platforms utilizes them all. Tunes are examined dependent on their advanced marks for certain elements, including rhythm, acoustics, energy, danceability, and so forth, to answer that incomprehensible old first-date inquiry. Organizations these days use music arrangement, either to have the option to put suggestions to their clients (like Spotify, Soundcloud) or just as an item (for instance, Shazam). Deciding music sorts is the initial phase toward that path. AI procedures have ended up being very fruitful in removing patterns and examples from a huge information pool. Similar standards are applied in Music Analysis moreover. Machine learning techniques are achieved in some recent years and rarely in deep learning. Most of the current music genre classification uses Machine learning techniques. In this, we present a music dataset which includes many genres like Rock, Pop, folk, Classical and many genres. A Deep learning approach is used in order to train and classify the system using KNN.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Ensemble of deep learning, visual and acoustic features for music genre classification

2018 ◽  
Vol 47 (4) ◽  
pp. 383-397 ◽  
Author(s):  
Loris Nanni ◽  
Yandre M. G. Costa ◽  
Rafael L. Aguiar ◽  
Carlos N. Silla ◽  
Sheryl Brahnam
Keyword(s):  
Deep Learning ◽  
Acoustic Features ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

scholarly journals Deep Learning for Generic Object Detection: A Survey

2019 ◽  
Vol 128 (2) ◽  
pp. 261-318 ◽  
Author(s):  
Li Liu ◽  
Wanli Ouyang ◽  
Xiaogang Wang ◽  
Paul Fieguth ◽  
Jie Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Evolution ◽  
Feature Representation ◽  
Future Research ◽  
Context Modeling ◽  
Feature Representations ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Object Feature

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

scholarly journals Music Genre Classification using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 66-71
Author(s):  
Sheeba Fathima
Keyword(s):  
Neural Networks ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Music Production ◽  
Genre Classification ◽  
Learning Techniques ◽  
Network Topologies ◽  
Classification Tasks ◽  
Music Genre ◽  
Music Genre Classification

Many subjects are affected by digital music production., including music genre prediction. Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. Including music genre classification. In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to provide more statistical information to higher level neural networks; and 2) To bypass one or more layers, use shortcut connections. To perform classification, the KNN output is fed into another deep neural network. Our preliminary experimental results on the GTZAN data set show that the above two methods, especially the second one, can effectively improve classification accuracy when compared to two different network topologies.

scholarly journals BiDI: Using Machine Learning to collect and facilitate remote access to biomedical databases (Preprint)

2020 ◽  
Author(s):  
Eduardo Rosado ◽  
Miguel Garcia-Remesal Sr ◽  
Sergio Paraiso-Medina Sr ◽  
Alejandro Pazos Sr ◽  
Victor Maojo Sr
Keyword(s):  
Deep Learning ◽  
Text Processing ◽  
Biomedical Literature ◽  
Remote Access ◽  
Pubmed Central ◽  
Processing Pipeline ◽  
Learning Techniques ◽  
Different Types ◽  
Automatic Text ◽  
Biomedical Databases

BACKGROUND Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases. OBJECTIVE To address this issue we developed BiDI (Biomedical Database Inventory), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them in a seamless manner. METHODS We designed an ensemble of Deep Learning methods to extract database mentions. To train the system we annotated a set of 1,242 articles that included mentions to database publications. Such a dataset was used along with transfer learning techniques to train an ensemble of deep learning NLP models based on the task of database publication detection. RESULTS The system obtained an f1-score of 0.929 on database detection, showing high precision and recall values. Applying this model to the PubMed and PubMed Central databases we identified over 10,000 unique databases. The ensemble also extracts the web links to the reported databases, discarding the irrelevant links. For the extraction of web links the model achieved a cross-validated f1-score of 0.908. We show two use cases, related to “omics” and the COVID-19 pandemia. CONCLUSIONS BiDI enables the access of biomedical resources over the Internet and facilitates data-driven research and other scientific initiatives. The repository is available at (http://gib.fi.upm.es/bidi/) and will be regularly updated with an automatic text processing pipeline. The approach can be reused to create repositories of different types (biomedical and others).

scholarly journals A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning

2019 ◽  
Vol 3 (1) ◽  
pp. 14 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Deep Learning ◽  
Future Research ◽  
3D Face Reconstruction ◽  
Research Directions ◽  
Facial Expression Analysis ◽  
Facial Landmark ◽  
Learning Techniques ◽  
In The Wild ◽  
Future Research Directions ◽  
2D Images

The task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided.

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