Abeßer, JakobJakobAbeßerBalke, StefanStefanBalkeFrieler, KlausKlausFrielerPfleiderer, MartinMartinPfleidererMüller, MeinardMeinardMüller2022-03-132024-04-152022-03-132017https://publica.fraunhofer.de/handle/publica/397973In this paper, we focus on transcribing walking bass lines, which provide clues for revealing the actual played chords in jazz recordings. Our transcription method is based on a deep neural network (DNN) that learns a mapping from a mixture spectrogram to a salience representation that emphasizes the bass line. Furthermore, using beat positions, we apply a late-fusion approach to obtain beat-wise pitch estimates of the bass line. First, our results show that this DNN-based transcription approach outperforms state-of-the-art transcription methods for the given task. Second, we found that an augmentation of the training set using pitch shifting improves the model performance. Finally, we present a semi-supervised learning approach where additional training data is generated from predictions on unlabeled datasets.enAutomatic Music Analysis621006conference paper