Abstract
In this paper, we adapt a recently proposed U-net deep neural network architecture from melody to bass transcription. We investigate pitch shifting and random equalization as data augmentation techniques. In a parameter importance study, we study the influence of the skip connection strategy between the encoder and decoder layers, the data augmentation strategy, as well as of the overall model capacity on the system’s performance. Using a training set that covers various music genres and a validation set that includes jazz ensemble recordings, we obtain the best transcription performance for a downscaled version of the reference algorithm combined with skip connections that transfer intermediate activations between the encoder and decoder. The U-net based method outperforms previous knowledge-driven and data-driven bass transcription algorithms by around five percentage points in overall accuracy. In addition to a pitch estimation improvement, the voicing estimation performance is clearly enhanced.
Highlights
The transcription of melodies and bass lines from complex music recordings is a challenging task for both human experts and machine algorithms
We want to study the influence of the data augmentation method, the skip connection type, as well as the network capacity of the U-net approach to the transcription performance on the validation set
The algorithm represents a model configuration, which is optimized for transcribing bass lines in jazz ensemble recordings
Summary
The transcription of melodies and bass lines from complex music recordings is a challenging task for both human experts and machine algorithms. If musical notes are simultaneously played on different instruments within a certain interval relationship, a subset of the resulting overtones overlap. This can result in pitch estimation mistakes such as octave errors. Both melodies and bass lines are typically monophonic and their estimation from audio recordings is considered as single-pitch estimation problems. In both scenarios, the transcription process involves two subproblems. The second subproblem is pitch estimation, where the fundamental frequency and its corresponding pitch is computed for each active frame
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