Despite their impressive offline results, deep learning models for symbolic music generation are not widely used in live performances due to a deficit of musically meaningful control parameters and a lack of structured musical form in their outputs. To address these issues we introduce LooperGP, a method for steering a Transformer-XL model towards generating loopable musical phrases of a specified number of bars and time signature, enabling a tool for live coding performances. We show that by training LooperGP on a dataset of 93,681 musical loops extracted from the DadaGP dataset, we are able to steer its generative output towards generating 3x as many loopable phrases as our baseline. In a subjective listening test conducted by 31 participants, LooperGP loops achieved positive median ratings in originality, musical coherence and loop smoothness, demonstrating its potential as a performance tool.

翻译：尽管深度学习的符号音乐生成模型在离线任务中取得了显著成果，但由于缺乏具有音乐意义的控制参数以及输出中缺少结构化的音乐形式，这些模型并未广泛应用于现场演奏。为了解决这些问题，我们提出了LooperGP方法，该方法能够引导Transformer-XL模型生成指定小节数和拍号的循环乐句，从而为即兴编码演奏提供工具支持。研究表明，通过在从DadaGP数据集中提取的93,681条音乐循环数据上训练LooperGP，我们能够引导其生成比基线模型多3倍的循环乐句。在31名参与者进行的主观聆听测试中，LooperGP生成的循环乐句在原创性、音乐连贯性和循环平滑度方面均获得正向中位数评分，验证了其作为演奏工具的潜力。