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Creators/Authors contains: "Salem, Alexandra C"

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  1. ; ; (, NeurIPS 2025 Workshop on AI for Music (AI4Music))
    We evaluate five Transformer-based strategies for chord-conditioned melody and bass generation using a set of music theory–motivated metrics capturing pitch content, pitch interval size, and chord tone usage. The evaluated models include (1) no chord conditioning, (2) independent line chord-conditioned generation, (3) bass-first chord-conditioned generation, (4) melody-first chord-conditioned generation, and (5) chord-conditioned co-generation. We show that chord-conditioning improves the replication of stylistic pitch content and chord tone usage characteristics, particularly for the bass-first model. 
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    Free, publicly-accessible full text available December 7, 2026
  2. ; ; ; ; (, Proceedings of IEEE Big Data 2025 (Presented at 3rd Workshop on AI Music Generation, AIMG 2025))
    In this paper, we introduce Story2MIDI, a sequence-to-sequence Transformer-based model for generating emotion-aligned music from a given piece of text. To develop this model, we construct the Story2MIDI dataset by merging existing datasets for sentiment analysis from text and emotion classification in music. The resulting dataset contains pairs of text blurbs and music pieces that evoke the same emotions in the reader or listener. Despite the small scale of our dataset and limited computational resources, our results indicate that our model effectively learns emotion-relevant features in music and incorporates them into its generation process, producing samples with diverse emotional responses. We evaluate the generated outputs using objective musical metrics and a human listening study, confirming the model’s ability to capture intended emotional cues. 
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    Free, publicly-accessible full text available December 9, 2026