Gomez, Juan S.Juan S.GomezAbeßer, JakobJakobAbeßerCano, EstefaníaEstefaníaCano2022-03-142024-04-122022-03-142018https://publica.fraunhofer.de/handle/publica/403850Predominant instrument recognition in ensemble recordings remains a challenging task, particularly if closely related instruments such as alto and tenor saxophone need to be distinguished. In this paper, we build upon a recently proposed instrument recognition algorithm based on a hybrid deep neural network: a combination of convolutional and fully connected layers for learning characteristic spectral-temporal patterns. We systematically evaluate harmonic/percussive and solo/accompaniment source separation algorithms as pre-processing steps to reduce the overlap among multiple instruments prior to the instrument recognition step. For the particular use-case of solo instrument recognition in jazz ensemble recordings, we further apply transfer learning techniques to fine-tune a previously trained instrument recognition model for classifying six jazz solo instruments. Our results indicate that both source separation as pre-processing step as well as transfer learning clearly improve recognition performance, especially for smaller subsets of highly similar instruments.enAutomatic Music Analysis621006conference paper