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MISNA - A musical instrument segregation system from noisy audio with LPCC-S features and extreme learning

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Abstract

Technology has developed a lot over the last decades and has made a profound impact in almost every field. The field of Music Information Retrieval (MIR) has not been an exception to this as well, one of its most promising applications being Automatic Music Transcription (AMT). It is important to identify the active regions of various Instruments in a piece before transcription and the challenge elevates even more when the audio clips are contaminated with noise. MISNA (Musical Instrument Segregation from Noisy Clips) is a system proposed towards the identification of isolated Instruments from noisy clips which can aid towards AMT in noisy environments. The system works using statistical features (LPCC-S) derived from raw Linear Predictive Cepstral Coefficient values on very short clips of lengths 1 and 2 seconds. The system has been tested for various SNR scenarios and highest accuracies of 98.63% and 97.42% for Individual Instruments and Instrument Family identification has been obtained with the aid of Extreme Learning based classifier for a highest of 2626 clips.

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Authors and Affiliations

  1. Department of Computer Science, West Bengal State University, Kolkata, India

    Himadri Mukherjee & Kaushik Roy

  2. Department of Computer Science and Engineering, Aliah University, Kolkata, India

    Sk Md Obaidullah

  3. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Santanu Phadikar

Authors
  1. Himadri Mukherjee
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  2. Sk Md Obaidullah
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  3. Santanu Phadikar
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  4. Kaushik Roy
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Corresponding author

Correspondence to Himadri Mukherjee.

Appendix

Appendix

Fig. 7
figure 7

a Feature values for the Instruments for 1 second long Clips in Fan Noise Condition. b Feature values for the Instruments for 2 second long Clips in Fan Noise Condition. c Feature values for the Instruments for 1 second long Clips in Rain Condition. d Feature values for the Instruments for 2 second long Clips in Rain Noise Condition

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Table 11 Individual Instrument Confusions for both D1(1s) and D2(2s)
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Fig. 8
figure 8

a Feature values for the Instruments for 1 second long Clips in Traffic Noise Condition. b Feature values for the Instruments for 2 second long Clips in Traffic Noise Condition. c Feature values for the Instruments for 1 second long Clips in Vacuum Cleaner Condition. d Feature values for the Instruments for 2 second long Clips in Vacuum Cleaner Noise Condition

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Table 12 Individual Instrument Confusions for both D3(1s) and D7(2s)
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Table 13 Individual Instrument Confusions for both D4(1s) and D8(2s)
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Table 14 Individual Instrument Confusions for both D5(1s) and D9(2s)
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Table 15 Individual Instrument Confusions for both D6(1s) and D10(2s)
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Table 16 (a) Instrument Family Confusions for both D1(1s) and D2(2s)
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Mukherjee, H., Obaidullah, S.M., Phadikar, S. et al. MISNA - A musical instrument segregation system from noisy audio with LPCC-S features and extreme learning. Multimed Tools Appl 77, 27997–28022 (2018). https://doi.org/10.1007/s11042-018-5993-6

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  • DOI: https://doi.org/10.1007/s11042-018-5993-6

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