Vishnu Srinivasa Murthy Yarlagadda

Orcid: 0000-0001-6146-5272

According to our database1, Vishnu Srinivasa Murthy Yarlagadda authored at least 13 papers between 2015 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2021
Singer identification for Indian singers using convolutional neural networks.
Int. J. Speech Technol., 2021

A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm.
Evol. Intell., 2021

A node to node security for sensor nodes implanted in cross cover multi-layer architecture using Mc-Nie algorithm.
Evol. Intell., 2021

2019
Academic Curriculum Load Balancing using GA.
Proceedings of the 10th International Conference on Computing, 2019

2018
Choice of a classifier, based on properties of a dataset: case study-speech emotion recognition.
Int. J. Speech Technol., 2018

Classification of vocal and non-vocal segments in audio clips using genetic algorithm based feature selection (GAFS).
Expert Syst. Appl., 2018

Content-Based Music Information Retrieval (CB-MIR) and Its Applications toward the Music Industry: A Review.
ACM Comput. Surv., 2018

Singer Identification from Smaller Snippets of Audio Clips Using Acoustic Features and DNNs.
Proceedings of the 2018 Eleventh International Conference on Contemporary Computing, 2018

2017
Dravidian language classification from speech signal using spectral and prosodic features.
Int. J. Speech Technol., 2017

Vocal and Non-vocal Segmentation based on the Analysis of Formant Structure.
Proceedings of the Ninth International Conference on Advances in Pattern Recognition, 2017

An approach to maintain attendance using image processing techniques.
Proceedings of the Tenth International Conference on Contemporary Computing, 2017

2016
Detection of largest possible repeated patterns in Indian audio songs using spectral features.
Proceedings of the 2016 IEEE Canadian Conference on Electrical and Computer Engineering, 2016

2015
Classification of vocal and non-vocal regions from audio songs using spectral features and pitch variations.
Proceedings of the IEEE 28th Canadian Conference on Electrical and Computer Engineering, 2015


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