In 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.

Authors: Abeßer, Jakob; Balke, Stefan; Frieler, Klaus; Pfleiderer, Martin; Müller, Meinard
Affiliations: Semantic Music Technologies Group, Fraunhofer IDMT,  Germany; International Audio Laboratories Erlangen, Erlangen, Germany; University of Music Franz Liszt, Weimar, Germany(See document for exact affiliation information.)
AES Conference: 2017 AES International Conference on Semantic Audio (June 2017)
Paper Number: 5-2
Publication Date: June 13, 2017 Import into BibTeX
Subject: Deep Learning
Permalink: http://www.aes.org/e-lib/browse.cfm?elib=18762

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