Separation and Classification of Different Musical Instruments Sound - Smita, Silk;Solanki, Sandeep Singh
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Musical instrument separation uses a source separation approach that aims to separate the individual source instruments from a mixture of sound signal i.e song. This work uses mixtures of two instruments guitar and flute, having same pitch at the same time. In this work, the degenerate blind source separation(DBSS) is modified and it is used for machine learning problem. This approach is applied to separate the instrument sounds. Further on the musical instrument sounds i.e separated signals are classified individually with Fuzzy approach. Temporal, spectral and cepstral features are extracted…mehr

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Produktbeschreibung
Musical instrument separation uses a source separation approach that aims to separate the individual source instruments from a mixture of sound signal i.e song. This work uses mixtures of two instruments guitar and flute, having same pitch at the same time. In this work, the degenerate blind source separation(DBSS) is modified and it is used for machine learning problem. This approach is applied to separate the instrument sounds. Further on the musical instrument sounds i.e separated signals are classified individually with Fuzzy approach. Temporal, spectral and cepstral features are extracted for instrument classification out of detected monophonic musical signals. Here, supervised fuzzy classification is used for instrument classification. The book is of interest for music and speech signal researchers.
Autorenporträt
Silk Smita,has Post Graduated from Birla Institute of Technology,Mesra,India in 2015.Her field of interest is Signal Processing,Classical Music, Instrumentation and Control.Sandeep Singh Solanki,Associate professor,Birla Institute of Technology,Mesra,India.Her field of interest is Signal Processing, Audio Signal Processing and Automation.