Self Organizing Feature Map Network for Musical Instrument Sounds
Gulhane Sushen R1, Shirbahadurkar Suresh D2, Badhe Sanjay S3
1Mr. Gulhane Sushen R., Research Scholar ( DYPIT), DYPCOE (SPPU), Pune, India.
2Dr. Shirbahadurkar Suresh D., Research Guide ( DYPIT) , Zeal COE (SPPU), Pune, India.
3Mr. Badhe Sanjay S., Research Scholar (DYPIT), DYPCOE (SPPU), Pune, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 16 July 2019 | Manuscript Published on 23 August 2019 | PP: 143-146 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I30290789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3029.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Self Organizing Feature Map Network is used as a classifier for classification of features extracted of the sound database of Musical Instruments. However, the database of Indian Classical Musical Instruments is prepared for 15 instruments. The different types of features such as Temporal, Spectral and Cepstral features are available out of which we have considered Spectral features i.e. Roll off, Centroid, RMS Energy, Zero Crossing Rate, Spectral Irregularity & Spectral Brightness.
Keywords: Roll off, Centroid, RMS Energy, Zero Crossing Rate, Spectral Irregularity & Brightness.
Scope of the Article: Digital Signal Processing Theory