In particular, we investigate the effects of learning on different splits of cross-version datasets, i.e., datasets that contain multiple recordings of the same pieces. To improve our understanding of these systems, we systematically explore network architectures for both global and local estimation, with varying depths and filter shapes, as well as different ways of splitting datasets for training, validation, and testing. We find that the same kinds of networks can also be used for key estimation by changing the orientation of directional filters. This allows us to take a purely data-driven approach using supervised machine learning (ML) with convolutional neural networks (CNN). We then re-formulate the signal-processing pipeline as a deep computational graph with trainable weights. We first propose novel methods using digital signal processing and traditional feature engineering.

To improve tempo estimation, we focus mainly on shortcomings of existing approaches, particularly estimates on the wrong metrical level, known as octave errors. Both tasks are well established in MIR research. Key estimation labels music recordings with a chord name describing its tonal center, e.g., C major.

Tempo estimation is often defined as determining the number of times a person would “tap” per time interval when listening to music. In this thesis, we propose, explore, and analyze novel data-driven approaches for the two MIR analysis tasks tempo and key estimation for music recordings. Creating such methods is a central part of the research area Music Information Retrieval (MIR). Efficient retrieval from such collections, which goes beyond simple text searches, requires automated music analysis methods. In recent years, we have witnessed the creation of large digital music collections, accessible, for example, via streaming services.

Sonic visualiser source code