Unsupervised Transcription Of Piano Music

1835 Words Mar 14th, 2015 8 Pages
Unsupervised Transcription of Piano Music

MS Technical Paper

Fei Xiang
Mar.14, 2015

1. Motivation
Audio signal processing has been a very active research area. Automatic piano music transcription, of all the tasks in this area, is an especially interesting and challenging one. There are many examples of how this technique can contribute to our life. For instance, in today’s music lessons and tests, we often rely on people’s hearing ability to judge whether a piano player performed well based on whether the notes played are accurate or not. The process requires man-power and is not always fair and accurate because people’s judgement is subjective. If a good automatic transcription system can be designed and implemented with high
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Most research work has been improving on this baseline in the one of the following two ways: better modeling of the discrete musical structure of the piece being transcribed [2,3] or by better adapting to the timbral properties of the source instrument [4,5].

Combining the above two approaches are difficult. Hidden Markov or semi-Markov models are widely used as the standard approach to model discrete musical structures. This approach needs fast dynamic programming for inference. To combine discrete models with timbral adaption and source separation, it would break the conditional independence assumptions that dynamic programming rely on. Previous research work to avoid this inference problem typically postpones detailed modeling the discrete structure of timbre to a post-processing step [6,7,8].

In a more recent work by Berg-Kirkpatrick et al., a novel approach is proposed to tackle the discrete and timbral modeling problems jointly. This is the first approach to model discrete and timbre at the same time without postponing one to the post-processing. Results showed this approach outperformed other models on multiple metrics.

In the following subsections, we will examine in details how each approach is trying to solve the problem and their results correspondingly.

2.1 Discrete Structure Modeling Approach
In a research work done by Nakano et. al.

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