Attention-based Transformer models have been increasingly employed for automatic music generation. To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a priming sequence and ask a Transformer decoder to generate a continuation. However, this prompt-based conditioning cannot guarantee that the conditioning sequence would develop or even simply repeat itself in the generated continuation. In this paper, we propose an alternative conditioning approach, called theme-based conditioning, that explicitly trains the Transformer to treat the conditioning sequence as a thematic material that has to manifest itself multiple times in its generation result. This is achieved with two main technical contributions. First, we propose a deep learning-based approach that uses contrastive representation learning and clustering to automatically retrieve thematic materials from music pieces in the training data. Second, we propose a novel gated parallel attention module to be used in a sequence-to-sequence (seq2seq) encoder/decoder architecture to more effectively account for a given conditioning thematic material in the generation process of the Transformer decoder. We report on objective and subjective evaluations of variants of the proposed Theme Transformer and the conventional prompt-based baseline, showing that our best model can generate, to some extent, polyphonic pop piano music with repetition and plausible variations of a given condition.

This paper deals with a score–audio music retrieval task where the aim is to find relevant audio recordings of Western classical music, given a short monophonic musical theme in symbolic notation as a query. Strategies for comparing score and audio data are often based on a common mid-level representation, such as chroma features, which capture melodic and harmonic properties. Recent studies demonstrated the effectiveness of neural networks that learn task-specific mid-level representations. Usually, such supervised learning approaches require score–audio pairs where the score's individual note events are aligned to the corresponding time positions of the audio excerpt. However, in practice, it is tedious to generate such strongly aligned training pairs. As one contribution, we show how to apply the Connectionist Temporal Classification (CTC) loss in the training procedure, which only uses weakly aligned training pairs. In such a pair, only the time positions of the beginning and end of a theme occurrence are annotated in an audio recording, rather than requiring local alignment annotations. We evaluate the resulting features in our theme retrieval scenario and show that they improve the state of the art for this task. As a main result, we demonstrate that with the CTC-based training procedure using weakly annotated data, we can achieve results almost as good as with strongly annotated data. Furthermore, we assess our chroma features in depth by inspecting their temporal smoothness or granularity as an important property and by analyzing the impact of different degrees of musical complexity on the features.

This paper introduces the Wagner Ring Dataset (WRD), a multi-modal and multi-version resource on the large-scale opera cycle Der Ring des Nibelungen by Richard Wagner. The Ring comprises four music dramas organized into eleven acts and 21 939 measures in total. Concerning sheet music, we processed a publicly available piano reduction (822 pages) of the full score with optical music recognition followed by extensive manual corrections to create a high-quality, machine-readable symbolic score. Concerning audio data, our corpus covers 16 recorded performances of the full Ring (three of which are publicly available thanks to copyright expiry), each lasting about 14–15 hours. To musically synchronize these versions among each other, we manually annotated all measure positions for three performances, which we transferred to the remaining performances via automated synchronization techniques. The dataset further comprises annotations of key and time signatures, scenes, and singing voice regions (libretto). Moreover, we provide note event annotations for all performances derived from the piano score. The WRD thus constitutes a comprehensive resource for developing algorithms for various music information retrieval tasks, complementing existing datasets with a complex opera scenario. For computational musicology, the WRD serves as a structured dataset that allows for studying the composition and performances of the Ring.

Musical themes are essential elements in Western classical music. In this paper, we present the Musical Theme Dataset (MTD), a multimodal dataset inspired by "A Dictionary of Musical Themes" by Barlow and Morgenstern from 1948. For a subset of 2067 themes of the printed book, we created several digital representations of the musical themes. Beyond graphical sheet music, we provide symbolic music encodings, audio snippets of music recordings, alignments between the symbolic and audio representations, as well as detailed metadata on the composer, work, recording, and musical characteristics of the themes. In addition to the data, we also make several parsers and web-based interfaces available to access and explore the different modalities and their relations through visualizations and sonifications. These interfaces also include computational tools, bridging the gap between the original dictionary and music information retrieval (MIR) research. The dataset is of relevance for various subfields and tasks in MIR, such as cross-modal music retrieval, music alignment, optical music recognition, music transcription, and computational musicology.

Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.