Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio

Instrument recognition in polyphonic audio signals is a very challenging classification task. It helps to improve related application scenarios, like music transcription and recommendation, organization of large music collections, or analysis of historical trends and properties of musical styles. Recently, the classification performance could be improved by the integration of deep convolutional neural networks. However, in to date published studies, the network architectures and parameter settings were usually adopted from image recognition tasks and manually adjusted, without a systematic optimization. In this paper, we show how two different neural architecture search strategies can be successfully applied for improvement of the prediction of nine instrument classes, significantly outperforming the classification performance of three fixed baseline architectures from previous works. Although high computing efforts for model optimization are required, the training of the final architecture is done only once for later prediction of instruments in a possibly unlimited number of musical tracks.