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. Our results indicate that the proposed methodology can achieve state of the art performance on a well-known benchmark dataset, thus providing a deeper analysis of musical structure. To evaluate our approach, we designed an experiment that allowed us to compare its segmentation performance with that of the current state of the art algorithms for hierarchical segmentation. This unsupervised method can perform both the tasks of boundary detection and structural grouping, without the need of particular constraints that would limit the resulting segmentation. We propose a novel methodology for the hierarchical analysis of music structure that is based on graph theory and multi-resolution community detection. However, most of the current music segmentation algorithms in literature can only produce flat segmentations, meaning that they cannot segment music at different levels in order to reveal its hierarchical structure. The automatic detection of musical structure in audio recordings is one of the most challenging problems in the field of music information retrieval, since even human experts tend to disagree on the structural decomposition of a piece of music. Musical structures are indeed best retained by listeners when they form hierarchical patterns, with consequent implications on the appreciation of music and its performance. Human perception of musical structure is supposed to depend on the generation of hierarchies, which is inherently related to the actual organisation of sounds in music.

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