Recognition of musical instruments in intervals and chords

Abstract

Recognition of musical instruments in pieces of polyphonic music given as mp3- or wav-files is a difficult task because the onsets are unknown. Using source-filter models for sound separation is one approach. In this study, intervals and chords played by instruments of four families of musical instruments (strings, wind, piano, plucked strings) are used to build statistical models for the recognition of the musical instruments playing them by using the four high-level audio feature groups Absolute Amplitude Envelope (AAE), Mel-Frequency Cepstral Coefficients (MFCC) windowed and not-windowed as well as Linear Predictor Coding (LPC) to take also physical properties of the instruments into account (Fletcher, The physics of musical instruments, 2008). These feature groups are calculated for consecutive time blocks. Statistical supervised classification methods such as LDA, MDA, Support Vector Machines, Random Forest, and Boosting are used for classification together with variable selection (sequential forward selection).