Call-and-response is a musical technique that enriches the creativity of music, crafting coherent musical ideas that mirror the back-and-forth nature of human dialogue with distinct musical characteristics. Although this technique is integral to numerous musical compositions, it remains largely uncharted in automatic music composition. To enhance the creativity of machine-composed music, we first introduce the Call-Response Dataset (CRD) containing 19,155 annotated musical pairs and crafted comprehensive objective evaluation metrics for musical assessment. Then, we design a knowledge-enhanced learning-based method to bridge the gap between human and machine creativity. Specifically, we train the composition module using the call-response pairs, supplementing it with musical knowledge in terms of rhythm, melody, and harmony. Our experimental results underscore that our proposed model adeptly produces a wide variety of creative responses for various musical calls.

Most musical compositions utilize repetition as a fundamental element to create captivating aesthetic experiences. However, the potential of repetition in machine-learning-based algorithmic composition has not been thoroughly investigated. This article aims to make an initial attempt at repetition modeling by generating motif-level repetitions and integrating them into music through a combination of example-based and domain knowledge–based learning techniques. The article presents a new Motif-to-music Generation Model (MGM) that combines a motif-level repetition generator (MRG) and an outline-to-music generator (O2MG). To train this model, a new music repetition dataset (MRD) has been created, which includes 584,329 samples from various categories of motif repetition and 3,545 outline-music sequences from pop piano music. The MRG uses a Transformer encoder to learn the representation of music notes from MRD, while the repetition-aware learner in MRG takes advantage of the unique characteristics of repetitions based on music theory. The O2MG applies a novel outline-to-music learning strategy to learn the relationships among motif-level repetitions in the music and generate music based on these repetitions. The experiments show that MGM can generate a variety of beautiful repetitions with any given motif, improving the music quality and structure of machine-composed music.

Repetition, a basic form of artistic creation, appears in most musical works and delivers enthralling aesthetic experiences. However, repetition remains underexplored in terms of automatic music composition. As an initial effort in repetition modelling, this paper focuses on generating motif-level repetitions via domain knowledge-based and example-based learning techniques. A novel repetition transformer (R-Transformer) that combines a Transformer encoder and a repetition-aware learner is trained on a new repetition dataset with 584,329 samples from different categories of motif repetition. The Transformer encoder learns the representation among music notes from the repetition dataset; the novel repetition-aware learner exploits repetitions' unique characteristics based on music theory. Experiments show that, with any given motif, R-Transformer can generate a large number of variable and beautiful repetitions. With ingenious fusion of these high-quality pieces, the musicality and appeal of machine-composed music have been greatly improved.