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Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.
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Cited by 5 Pith papers
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Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production
A qualitative case study of nine novice music producers shows AI compresses the preparation stage, adds a new 'collaging and refinement' stage, and shifts group social dynamics, leading to two proposed models.
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Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
Self-supervised pretraining on 60,000 hours of symbolic piano music produces a generative model and contrastive embeddings that beat leading baselines on continuation quality and several MIR classification benchmarks.
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ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement
A corruption-refinement transformer, ImprovNet, generates controllable jazz and classical improvisations of complete piano pieces, and also handles harmonization, continuation, and infilling.
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Context-AI Tunes: Context-Aware AI-Generated Music for Stress Reduction
Context-aware AI-generated music produced larger self-reported stress reductions than manually chosen relaxing music across busy and quiet environments in a within-subject study of 26 participants.
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Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations
A human-AI songwriting tool that generates keyword-relevant chord progressions from images, text, or audio by filtering LLM suggestions with a chord model, shown to increase perceived agency and creativity in a small ...
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