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The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures

4 Aug 2020arXiv:2008.01307archive 2025-07-28

Shih-Lun Wu, Yi-Hsuan Yang

This paper presents the Jazz Transformer, a generative model that utilizes a neural sequence model called the Transformer-XL for modeling lead sheets of Jazz music. Moreover, the model endeavors to incorporate structural events present in the Weimar Jazz Database (WJazzD) for inducing structures in the generated music. While we are able to reduce the training loss to a low value, our listening test suggests however a clear gap between the average ratings of the generated and real compositions. We therefore go one step further and conduct a series of computational analysis of the generated compositions from different perspectives. This includes analyzing the statistics of the pitch class, grooving, and chord progression, assessing the structureness of the music with the help of the fitness scape plot, and evaluating the model's understanding of Jazz music through a MIREX-like continuation prediction task. Our work presents in an analytical manner why machine-generated music to date still falls short of the artwork of humanity, and sets some goals for future work on automatic composition to further pursue.

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collect_segments slSeanWU/jazz_transformer/src/convert_to_remi.py official repository unverified MIT (permissive) · aa1a0fcc8b6dfe82 · report
compute_SM_dot slSeanWU/MusDr/mueller_audio_tools/ssm_features.py official repository unverified MIT (permissive) · e0ab2086c54458d0 · report
compute_accumulated_score_matrix slSeanWU/MusDr/mueller_audio_tools/scapeplot.py official repository unverified MIT (permissive) · 5d740a70b2bc6c53 · report
compute_histogram_entropy slSeanWU/MusDr/musdr/side_utils.py official repository unverified MIT (permissive) · 1f26bd9136f2e20d · report
compute_optimal_path_family slSeanWU/MusDr/mueller_audio_tools/scapeplot.py official repository unverified MIT (permissive) · be870c68b8b96085 · report
embedding_lookup slSeanWU/jazz_transformer/transformer_xl/modules.py official repository unverified MIT (permissive) · 0a923f63ece46f08 · report
get_chord_sequence slSeanWU/MusDr/musdr/side_utils.py official repository unverified MIT (permissive) · 74c496bf558c63ae · report
get_event_seq slSeanWU/MusDr/musdr/side_utils.py official repository unverified MIT (permissive) · c77e1599951d2579 · report
get_note_duration_distr slSeanWU/jazz_transformer/src/explore_mcsv.py official repository unverified MIT (permissive) · 28d4744d03114079 · report
normal_embedding_lookup slSeanWU/jazz_transformer/transformer_xl/modules.py official repository unverified MIT (permissive) · 43ce03d635bf63b4 · report
normal_softmax slSeanWU/jazz_transformer/transformer_xl/modules.py official repository unverified MIT (permissive) · 71f9313c35afe0e6 · report
normalization_properties_SSM slSeanWU/MusDr/mueller_audio_tools/scapeplot.py official repository unverified MIT (permissive) · 970a6eb890d8917e · report
normalize_feature_sequence slSeanWU/MusDr/mueller_audio_tools/ssm_features.py official repository unverified MIT (permissive) · ed88321a270dd815 · report
smooth_downsample_feature_sequence slSeanWU/MusDr/mueller_audio_tools/ssm_features.py official repository unverified MIT (permissive) · ce3d8f5244b4f7ee · report

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Methods

Absolute Position EncodingsAdamAdaptive Input RepresentationsAdaptive SoftmaxAttentionBPECosine AnnealingDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmaxTransformerTransformer-XLVariational Dropout

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