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

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.