A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals

The authors address the identification of predominant music instruments in polytimbral audio by previously dividing the original signal into several streams. Several strategies are evaluated, ranging from low to high complexity with respect to the segregation algorithm and models used for classification. The dataset of interest is built from professionally produced recordings, which typically pose problems to state-of-art source separation algorithms. The recognition results are improved a 19% with a simple sound segregation pre-step using only panning information, in comparison to the original algorithm. In order to further improve the results, we evaluated the use of a complex source separation as a pre-step. The results showed that the performance was only enhanced if the recognition models are trained with the features extracted from the separated audio streams. In this way, the typical errors of state-of-art separation algorithms are acknowledged, and the performance of the original instrument recognition algorithm is improved in up to 32%.