{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:58:37Z","timestamp":1771844317985,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>To address the current challenges of deep learning music generation models in capturing long-range dependencies, ensuring generation diversity, and maintaining training stability, this study proposes an optimized music melody generation model\u2014T-WGAN. The model deeply integrates Transformer and Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). This study first preprocesses the large-scale Lakh MIDI dataset, extracts single-track main melodies, and converts them into symbolic sequences using a REMI-based event representation. On this basis, the model innovatively adopts a generator based on Transformer decoder to learn the long-range structure of melodies. It also uses a critic based on Transformer encoder for stable adversarial training under the WGAN-GP framework to enhance the diversity and authenticity of generated melodies. Experimental results show that T-WGAN performs excellently on multiple key evaluation metrics. T-WGAN achieves a Rhythmic Consistency Rate (RCR) of 85.17%, significantly higher than baseline models (e.g., Transformer\u2019s 75.68%). Its score on Fr\u00e9chet Distance for Music (FDM) drops to 31.02, proving that the generated melodies are closer to real music in feature distribution. The conclusion indicates that the proposed T-WGAN model successfully addresses the three core issues in melody generation\u2014structural integrity, diversity, and training stability\u2014synergistically. The findings provide an effective technical approach for generating high-quality music melodies with both structural logic and innovation.<\/jats:p>","DOI":"10.31449\/inf.v50i8.11624","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T11:57:26Z","timestamp":1771761446000},"source":"Crossref","is-referenced-by-count":0,"title":["T-WGAN: A Transformer-Wasserstein GAN Approach for Melody Generation with Structural and Rhythmic Fidelity"],"prefix":"10.31449","volume":"50","author":[{"given":"Chunxiao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11624\/6540","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11624\/6540","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:01:29Z","timestamp":1771840889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i8.11624","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}