Optimum Feature Selection for Harmonium Note Identification Using ANN

The harmonium music notes exhibit multiple variations so the selection of the optimal features for the musical note identification is difficult. To improve identification accuracy, the selection of the important features is necessary. In this paper, an artificial neural network (ANN) based automatic...

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Bibliographic Details
Published in2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) pp. 1 - 7
Main Authors Puri, Surekha B., Mahajan, Shrinivas P
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
Acoustic Model
ANN
Artificial neural networks
Automatic Music Transcription
Correlation
Feature extraction
Filter banks
Indian Classical Music
Mel frequency cepstral coefficient
Microsoft Windows
Music
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Summary:The harmonium music notes exhibit multiple variations so the selection of the optimal features for the musical note identification is difficult. To improve identification accuracy, the selection of the important features is necessary. In this paper, an artificial neural network (ANN) based automatic harmonium musical note identification approach is proposed by considering different features and their combinations. The feature was extracted using MFCC, LPC and other analytical methods. In this paper, 35 audio features were extracted from the harmonium signal, which was then trained and tested by ANN with different combinations of features, which gives better accuracy. Some combinations like MFCC with CQT give 100% accuracy, while most others give above 90% accuracy. This method achieves the promising results on the self-generated database. The audio recordings were collected from professional harmonium artist in a varied environment.
DOI:10.1109/ICCCNT45670.2019.8944560