Allabakash Tamboli

@sggs.ac.in

SGGS INSTITUTE OF ENGINEERING AND TECHNOLOGY VISHNUPURI NANDED, ASSOCIATE PROFESSOR
SGGS Institute of Engineering and Technology



              

https://researchid.co/aitamboli

EDUCATION

Ph.D. in Electronics and Telecommunication Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering

2

Scopus Publications

Scopus Publications

  • Query based relevant music genre retrieval using adaptive artificial neural network for multimedia applications
    Allabakash Isak Tamboli and Rajendra D. Kokate
    Springer Science and Business Media LLC

  • An Effective Optimization-Based Neural Network for Musical Note Recognition
    Allabakash Isak Tamboli and Rajendra D. Kokate
    Walter de Gruyter GmbH
    Abstract Musical pitch estimation is used to recognize the musical note pitch or the fundamental frequency (F 0) of an audio signal, which can be applied to a preprocessing part of many applications, such as sound separation and musical note transcription. In this work, a method for musical note recognition based on the classification framework has been designed using an optimization-based neural network (OBNN). A broad range of survey and research was reviewed, and all revealed the methods to recognize the musical notes. An OBNN is used here in recognizing musical notes. Similarly, we can progress the effectiveness of musical note recognition using different methodologies. In this document, the most modern investigations related to musical note recognition are effectively analyzed and put in a nutshell to effectively furnish the traits and classifications.

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