Musical Instrument Recognition using Spectrogram and Autocorrelation
Sumit Kumar Banchhor1, Arif Khan2
1Sumit Kumar Banchhor, Electronics and Telecommunication, Chhattisgarh Swami Vivekananda Technical University, GD Rungta College of Engineering and Technology, Bhilai, India.
2Arif Khan, Electronics and Telecommunication, Chhattisgarh Swami Vivekananda Technical University, GD Rungta College of Engineering and Technology, Bhilai, India.
Manuscript received on February 09, 2012. | Revised Manuscript received on February 15, 2012. | Manuscript published on March 05, 2012. | PP: 1-4 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0366012111/2012©BEIESP
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© The Authors. Published By: 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: Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (flute, guitar and harmonium), which yields a high recognition rate. A large solo database is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. The basic characteristics are computed in 1sec interval and result shows that the estimation of spectrogram and autocorrelation reflects more effectively the difference in musical instruments.
Keywords: Speech/music classification, audio segmentation, spectrogram, autocorrelation.