In musicology, there has been a long debate about a meaningful partitioning and description of music history regarding composition styles. Particularly, concepts of historical periods have been criticized since they cannot account for the continuous and interwoven evolution of style. To systematically study this evolution, large corpora are necessary suggesting the use of computational strategies. This article presents such strategies and experiments relying on a dataset of 2000 audio recordings, which cover more than 300 years of music history. From the recordings, we extract different tonal features. We propose a method to visualize these features over the course of history using evolution curves. With the curves, we re-trace hypotheses concerning the evolution of chord transitions, intervals, and tonal complexity. Furthermore, we perform unsupervised clustering of recordings across composition years, individual pieces, and composers. In these studies, we found independent evidence of historical periods that broadly agrees with traditional views as well as recent data-driven experiments. This shows that computational experiments can provide novel insights into the evolution of styles.

This article presents a multimodal dataset comprising various representations and annotations of Franz Schubert’s song cycle Winterreise . Schubert’s seminal work constitutes an outstanding example of the Romantic song cycle—a central genre within Western classical music. Our dataset unifies several public sources and annotations carefully created by music experts, compiled in a comprehensive and consistent way. The multimodal representations comprise the singer’s lyrics, sheet music in different machine-readable formats, and audio recordings of nine performances, two of which are freely accessible for research purposes. By means of explicit musical measure positions, we establish a temporal alignment between the different representations, thus enabling a detailed comparison across different performances and modalities. Using these alignments, we provide for the different versions various musicological annotations describing tonal and structural characteristics. This metadata comprises chord annotations in different granularities, local and global annotations of musical keys, and segmentations into structural parts. From a technical perspective, the dataset allows for evaluating algorithmic approaches to tasks such as automated music transcription, cross-modal music alignment, or tonal analysis, and for testing these algorithms’ robustness across songs, performances, and modalities. From a musicological perspective, the dataset enables the systematic study of Schubert’s musical language and style in Winterreise and the comparison of annotations regarding different annotators and granularities. Beyond the research domain, the data may serve further purposes such as the didactic preparation of Schubert’s work and its presentation to a wider public by means of an interactive multimedia experience. With this article, we provide a detailed description of the dataset, indicate its potential for computational music analysis by means of several studies, and point out possibilities for future research.

Extracting pitch information from music recordings is a challenging but important problem in music signal processing. Frame-wise transcription or multi-pitch estimation aims for detecting the simultaneous activity of pitches in polyphonic music recordings and has recently seen major improvements thanks to deep-learning techniques, with a variety of proposed model architectures. In this paper, we compare different architectures based on convolutional neural networks, the U-net structure, and self-attention components. We propose several modifications to these architectures including self-attention modules for skip connections, recurrent layers to replace the self-attention, and a multi-task strategy with simultaneous prediction of the degree of polyphony. We compare variants of these architectures in different sizes for multi-pitch estimation, focusing on Western classical music beyond the piano-solo scenario using the MusicNet and Schubert Winterreise datasets. Our experiments indicate that most architectures yield competitive results and that larger model variants seem to be beneficial. However, we find that these results substantially depend on randomization effects and the particular choice of the training–test split, which questions the claim of superiority for particular architectures given only small improvements. We therefore investigate the influence of dataset splits in the presence of several movements of a work cycle (cross-version evaluation) and propose a best-practice evaluation strategy for MusicNet, which weakens the influence of individual test tracks and suppresses overfitting to specific works and recording conditions. A final cross-dataset evaluation suggests that improvements on one specific dataset do not necessarily generalize to other scenarios, thus emphasizing the need for further high-quality multi-pitch datasets in order to reliably measure progress in music transcription tasks.

Despite major advances in recent years, the timely detection and prevention of incipient congestion in patients with chronic heart failure remains challenging. Most approaches are either invasive or require the acquisition of additional hardware. Leveraging voice analysis for the purposes of diagnosing, predicting risks, and telemonitoring clinical outcomes of patients with heart failure represents a promising, cost-effective, and convenient alternative compared with hitherto deployed methods. To expand this field, close collaboration of several disciplines of medicine and computer science is an obligatory requirement. The current review aims to lay out the state-of-the-art in this quickly advancing area of research. It elucidates the foundation for voice feature extraction, describes the prospective capabilities of this evolving technology, and outlines the challenges involved in identifying vocal biomarkers both in general and in the context of heart failure.

We propose a set of novel audio features for classifying the style of classical music. The features rely on statistical measures based on a chroma feature representation of the audio data and describe the tonal complexity of the music, independently from the orchestration or timbre of the music. To analyze this property, we use a dataset containing piano and orchestral music from four general historical periods including Baroque, Classical, Romantic, and Modern. By applying dimensionality reduction techniques, we derive visualizations that demonstrate the discriminative power of the features with regard to the music styles. In classification experiments, we evaluate the features' performance using an SVM classifier. We investigate the influence of artist filtering with respect to the individual composers on the classification performance. In all experiments, we compare the results to the performance of standard features. We show that the introduced features capture meaningful properties of musical style and are robust to timbral variations.