scholarly journals Hybrid Feature Based Classifier Performance Evaluation of Monophonic and Polyphonic Indian Classical Instruments Recognition

2021 ◽  
Vol 23 (11) ◽  
pp. 879-890
Author(s):  
Abhijit V. Chitre ◽  
◽  
Ketan J. Raut ◽  
Tushar Jadhav ◽  
Minal S. Deshmukh ◽  
...  
Keyword(s):  
Critical Role ◽  
Musical Instrument ◽  
Research Area ◽  
Musical Instruments ◽  
Training Data ◽  
Hybrid Features ◽  
Spectral Parameters ◽  
Data Set ◽  
Important Research Area ◽  
Musical Sounds

Instrument recognition in computer music is an important research area that deals with sound modelling. Musical sounds comprises of five prominent constituents which are Pitch, timber, loudness, duration, and spatialization. The tonal sound is function of all these components playing critical role in deciding quality. The first four parameters can be modified, but timbre remains a challenge [6]. Then, inevitably, timbre became the focus of this piece. It is a sound quality that distinguishes one musical instrument from another, regardless of pitch or volume, and it is critical. Monophonic and polyphonic recordings of musical instruments can be identified using this method. To evaluate the proposed approach, three Indian instruments were experimented to generate training data set. Flutes, harmoniums, and sitars are among the instruments used. Indian musical instruments classify sounds using statistical and spectral parameters. The hybrid features from different domains extracting important characteristics from musical sounds are extracted. An Indian Musical Instrument SVM and GMM classifier demonstrate their ability to classify accurately. Using monophonic sounds, SVM and Polyphonic produce an average accuracy of 89.88% and 91.10%, respectively. According to the results of the experiments, GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

RF-Identity

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Chao Feng ◽  
Jie Xiong ◽  
Liqiong Chang ◽  
Fuwei Wang ◽  
Ju Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Frequency Bandwidth ◽  
Person Identification ◽  
Data Set ◽  
Rf Devices ◽  
Large Frequency

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

scholarly journals MUSICALISATION OF J. BUNYAN’S AND H. SKOVORODA’S LITERARY DISCOURSE

2019 ◽  
pp. 372-384
Author(s):  
O. Shykyrynska
Keyword(s):  
Significant Influence ◽  
Musical Instrument ◽  
Musical Instruments ◽  
Common Features ◽  
Musical Space ◽  
The Bible ◽  
Musical Theme ◽  
The World ◽  
Musical Sounds ◽  
Artistic Heritage

The article deals with the musical space of the artistic heritage of J. Bunyan and H. Skovoroda that has many common features. The general place in the heritage of both writers is reference to solemn church or angelic singing, accompanying the scenes of triumph of the heroes. There are numerous quotations from the Bible psalms, that both writers mastered perfectly. Outplaying of the mythologemes “a man as a musical instrument” and “a world as a musical instrument” became common for both authors. Musical code is expressed in comparison with man’s features and musical sounds; assimilation of the world with a musical instrument, desire to hear “the music of spheres”. The comparison of a man’s emotional impulse with the sounds of musical instruments reveals willingness of the man of the Baroque age for the search of correspondence and for the synthesis of arts in a broad sense. Music as an art differs in the ability to reveal symbols by means of a sound, having a significant influence on the recipient. The analysis of musical component of H. Skovoroda and J. Bunyan’s work demonstrates its precise orientation on musicalisation of writers’ discourse. In the meantime musical theme is represented much wider in Skovoroda’s work than in the work of the English writer. The article introduces J. Bunyan and H. Skovoroda as bright representatives of national variants of baroque aesthetics.

ON COMPUTING THE FUZZIFIER IN ↓FLVQ: A DATA DRIVEN APPROACH

2002 ◽  
Vol 12 (02) ◽  
pp. 149-157 ◽  
Author(s):  
L. B. ROMDHANE ◽  
B. AYEB ◽  
S. WANG
Keyword(s):  
Fuzzy Clustering ◽  
Research Area ◽  
Book Design ◽  
Data Set ◽  
Practical Applications ◽  
Probabilistic Clustering ◽  
Data Driven Approach ◽  
Optimal Setting ◽  
Important Research Area

Clustering is an important research area that has practical applications in many fields. Fuzzy clustering has shown advantages over crisp and probabilistic clustering, especially when there are significant overlaps between clusters. Most analytic fuzzy clustering approaches are derived from Bezdek's fuzzy c-means algorithm. One major factor that influences the determination of appropriate clusters in these approaches is an exponent parameter, called the fuzzifier. To our knowledge, no theoretical reason leading to an optimal setting of this parameter is available. This paper presents the development of an heuristic scheme for determining the fuzzifier. This scheme creates close interactions between the fuzzifier and the data set to be clustered. Experimental results in clustering IRIS data and in code book design required for image compression reveal a good performance of our proposal.

EBPA: A NOVEL FRAMEWORK FOR ANALYSIS OF PROCESS PERFORMANCE ON BASIS OF REAL TIME NASA APPLICATION

2019 ◽  
Vol 12 ◽  
Author(s):  
Shashank Sharma ◽  
Sumit Srivastava
Keyword(s):  
Reference Model ◽  
Research Area ◽  
Workflow Model ◽  
Process Performance ◽  
Important Research ◽  
Performance Indices ◽  
Data Set ◽  
Workflow Mining ◽  
Event Log ◽  
Important Research Area

Workflow mining is an important research area of information systems designed for the specific organization as per their needs or challenges. The beginning stage of workflow mining purely based on the event log that extracted from a legacy information system. The learning got along these logs can build comprehension about the workflow of procedures and association of different processes. That can help with upgrading them if necessary. In this paper, we perform the extraction of the workflow model using workflow mining. This process is effective and efficient as compared with building workflow model from scratch. This paper displays a process performance-based framework where we compare the reference model with the extracted model based on key performance indices. This approach is not principally comprising of subjective investigation. Principle worry of subjective investigation is the legitimate rightness of procedures (nonattendance of oddities, similar to gridlocks and livelocks). The idea is conceptualized and validated by experimentation using a suitable data set. In this paper, the analysis that was finished amid the undertaking will be talked about, and the modules that have been executed will be depicted.

scholarly journals Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation

Buildings ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 239 ◽  
Author(s):  
Janghyun Kim ◽  
Stephen Frank ◽  
Piljae Im ◽  
James E. Braun ◽  
David Goldwasser ◽  
...  
Keyword(s):  
Cost Effective ◽  
Research Area ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fault Models ◽  
Commercial Building ◽  
Data Set ◽  
Modeling Software ◽  
Detection And Diagnosis

Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.

scholarly journals The Architectonics of Musical Sound

ICONI ◽  
2019 ◽  
pp. 112-128
Author(s):  
Irina B. Gorbunova ◽  
Keyword(s):  
Musical Instrument ◽  
Musical Instruments ◽  
Musical Composition ◽  
Basic Principles ◽  
Musical Sound ◽  
Music Making ◽  
Wide Range ◽  
Fast Development ◽  
Musical Sounds ◽  
Computer Generation

At the turn of the 20th and 21st centuries there appeared a new trend in musical composition and musical pedagogy conditioned by the fast development of electronic musical instruments: from the simplest synthesizers to powerful musical computers. In the wide range contemporary electronic musical instrument the accumulated informational technologies in music and the art of music making have manifested themselves in the fullest and most perfect manner. The current and the subsequent issues of the journal shall provide a consecutive presentation of four lectures, compiling the basis of the discipline “Informational Technologies in Music” and a set of programs of advanced training, which include “Informational Technologies in Music,” “Informational Technologies in Musical Education,” “Computer Musical Composition,” etc. The first lecture, “The Architectonics of Musical Sound” shall disclose themes connected with the study of the physical characteristics of musical sounds, the means of their recording and reproduction; explanation is given to the aural perception of sound by the human being, and the basic principles of computer generation of musical sound are examined. The material elucidated in the lection possesses a theoretical and practical directedness and contains information in which the technological aspects of contemporary perceptions of music, about the musical instrument range (including computer musical instruments); without knowledge of these aspects a competent interpretation of musical instruments is impossible.

scholarly journals Monophonic Musical Instrument Sound Classification Using Impulse Response Modeling

2021 ◽  
Vol 12 (5) ◽  
pp. 1667-1672
Author(s):  
Rutuja S Kothe, Et. al.
Keyword(s):  
Impulse Response ◽  
Research Work ◽  
Musical Instrument ◽  
Research Area ◽  
Musical Instruments ◽  
The Body ◽  
Sound Classification ◽  
Social Applications ◽  
Response Modeling ◽  
Body Response

The field of music has promising commercial and social applications. Hence it has attracted the attention of researchers, engineers, sociologists and health care peoples. Therefore this particular research area has been selected. In this manuscript the monophonic musical  classificationsystem using impulse response of the system is presented. In this research work 19  musical instruments monophonic sounds from 4  families are   classified using WEKA classifier. The impulse response is of all musical instruments and families are  computed in Cepstral Domain. AsImpulse response is used to model the body response of the musical instruments and helps to capture the information.  It is different for different instruments. The features are extracted from impulse response and presented to WEKA Classifier. The  Musical instrument classification for individual instruments and family is verified using impulse response modeling. It is found that the impulse response is different for different instruments. It helps to easily distinguish between instrument to instrument and family to family. For individual instruments, the average classification accuracy has been obtained is 83.23% and 85.55% for family classification.

An Introduction to Deep Convolutional Neural Networks With Keras

2020 ◽  
pp. 231-272
Author(s):  
Wazir Muhammad ◽  
Irfan Ullah ◽  
Mohammad Ashfaq
Keyword(s):  
Graphics Processing Units ◽  
Real Life ◽  
Research Area ◽  
Training Data ◽  
Training Procedure ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Life Problems ◽  
Graphics Processing ◽  
And Training

Deep learning (DL) is the new buzzword for researchers in the research area of computer vision that unlocked the doors to solving complex problems. With the assistance of Keras library, machine learning (ML)-based DL and various complicated or unresolved issues such as face recognition and voice recognition might be resolved easily. This chapter focuses on the basic concept of Keras-based framework DL library to handle the different real-life problems. The authors discuss the codes of previous libraries and same code run on Keras library and assess the performance on Google Colab Cloud Graphics Processing Units (GPUs). The goal of this chapter is to provide you with the newer concept, algorithm, and technology to solve the real-life problems with the help of Keras framework. Moreover, they discuss how to write the code of standard convolutional neural network (CNN) architectures using Keras libraries. Finally, the codes of validation and training data set to start the training procedure are explored.

Connectionist and Evolutionary Models for Learning, Discovering and Forecasting Software Effort

2011 ◽  
pp. 250-276
Author(s):  
Parag C. Pendharkar ◽  
Girish Subramanian
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Training Data ◽  
Great Promise ◽  
Evolutionary Models ◽  
Software Projects ◽  
Data Set ◽  
Large Databases ◽  
Very Large Databases ◽  
Learning Software

Mining information and knowledge from very large databases is recognized as a key research area in machine learning and expert systems. In the current research, we use connectionist and evolutionary models for learning software effort. Specifically, we use these models to learn the software effort from a set of training data set containing information on software projects and test the performance of the model on a holdout sample. The design issues of developing connectionist and evolutionary models for mining software effort patterns on a data set are described. Our research indicates that connectionist and evolutionary models, whenever carefully designed, hold a great promise for knowledge discovery and forecasting software effort.

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