Open Access Open Access  Restricted Access Subscription or Fee Access

Identification of individual ragas using artificial neural network classifier

Priya K., Ravi Kumar

Abstract


This paper presents the feature extraction as the base step of music information retrieval. Basic feature of music is chroma, which is extracted using from the music signal using spectrogram and chromagram. After the extraction of feature the comparison is made between the original data and extracted chroma data using neural network.

 

Cite this Article
Priya, Ravi Kumar. Identification of individual ragas using artificial neural network classifier. Journal of Artificial Intelligence Research & Advances. 2015; 2(2): 24–28p.


Keywords


Music information retrieval, artificial neural network, chromagram

Full Text:

PDF

References


Meinard Müller, Daniel P. W. Ellis, Anssi, Gaël Richard. Signal Processing for Music Analysis. IEEE Journal of Selected Topics in Signal Processing. 2011; 5(6): 1088–1110p.

Laurent Oudre, Cédric Févotte, Yves Grenier. Probabilistic Template-Based Chord Recognition. IEEE Transactions on Audio, Speech, and Language Processing. 2011; 19(8): 2249–.

2259pPasi Saari, Tuomas Eerola, Olivier Lartillot. Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music. IEEE Transactions on Audio, Speech, and Language Processing. 2011; 19(6): 1802–1812p.

Meinard Müller, Sebastian Ewert. Towards Timbre-Invariant Audio Features for Harmony-Based Music. IEEE Transactions on Audio, Speech, and Language Processing. 2010; 18(3): 649–662p.

Yonatan Vaizman, Brian McFee. Codebook-Based Audio Feature Representation for Music Information Retrieval. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2014; 22(10): 1483–1493p.

Olivier Lartillot, Petri. MIR in MATLAB (ii): a toolbox for musical feature extraction from audio. Proceedings Conference ISMIR. 2007: 127–13p.

Hideyuki Tachibana, Nobutaka Ono, Hirokazu Kameoka, Shigeki Sagayama. Harmonic/Percussive Sound Separation Based on Anisotropic Smoothness of Spectrograms. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2014; 22(12): 2059–2073p.

Pranay Dighe, Parul Agrawal, Harish Karnick, Siddartha Thota ,Bhiksha Raj. Scale Independent Raga Identification Using Chromagram Patterns And Swara Based Features. IEEE International Conference on Multimedia and Expo Workshops (ICMEW). 2013:1–4p.

Siddharth Sigtia, Simon Dixon. Improved Music Feature Learning with Deep Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).2014; May: 6959–6963p.

Benyamin Matityaho, Miriam Furst. Neural network based model for classification of music type. Eighteenth Convention of Electrical and Electronics Engineers in Israel.1995; March: 4.3.4/1–4.3.4/5p.


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/