Title :
Instrument recognition in accompanied sonatas and concertos
Author :
Eggink, Jana ; Brown, Guy J.
Author_Institution :
Dept. of Comput. Sci., Univ. of Sheffield, UK
Abstract :
A system for musical instrument recognition is introduced. In contrast to most existing systems, it can identify a solo instrument even in the presence of an accompanying keyboard instrument or orchestra. To enable recognition in the presence of a highly polyphonic background, we use features based solely on the partials of the target tone. The approach is based on the assumption that it is possible to extract the most prominent fundamental frequency and the corresponding harmonic overtone series, and that these most often belong to the solo instrument. Classification is carried out using a Gaussian classifier trained on examples of monophonic music. Testing our system on accompanied sonatas and concertos we achieved a recognition rate of 86% for 5 different instruments, an accuracy comparable to that of systems limited to monophonic music only.
Keywords :
Gaussian processes; audio signal processing; frequency estimation; music; musical instruments; signal classification; Gaussian classifier; accompanied sonatas; audio files; concertos; fundamental frequency estimation; fundamental frequency extraction; harmonic overtone series; instrument classification; monophonic music; musical instrument recognition; polyphonic background; solo instrument recognition; target tone partials; Acoustic noise; Computer science; Convolution; Frequency estimation; Friction; Instruments; Pattern matching; Power system harmonics;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
Print_ISBN :
0-7803-8484-9
DOI :
10.1109/ICASSP.2004.1326802