XMusic: Verso un Framework di Generazione Musicale Simbolica Generalizzato e Controllabile

XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework

January 15, 2025
Autori: Sida Tian, Can Zhang, Wei Yuan, Wei Tan, Wenjie Zhu
cs.AI

Abstract

Negli ultimi anni, sono stati raggiunti notevoli progressi nella generazione di contenuti tramite intelligenza artificiale (AIGC) nei campi della sintesi delle immagini e della generazione di testi, generando contenuti paragonabili a quelli prodotti dagli esseri umani. Tuttavia, la qualità della musica generata dall'IA non ha ancora raggiunto questo standard, principalmente a causa della sfida di controllare efficacemente le emozioni musicali e garantire output di alta qualità. Questo articolo presenta un framework generalizzato per la generazione di musica simbolica, XMusic, che supporta prompt flessibili (ad esempio immagini, video, testi, tag e canto) per generare musica simbolica emotivamente controllabile e di alta qualità. XMusic è composto da due componenti principali, XProjector e XComposer. XProjector analizza i prompt di varie modalità in elementi musicali simbolici (ad esempio emozioni, generi, ritmi e note) nello spazio di proiezione per generare musica corrispondente. XComposer contiene un Generatore e un Selettore. Il Generatore genera musica emotivamente controllabile e melodiosa basata sulla nostra innovativa rappresentazione della musica simbolica, mentre il Selettore identifica musica simbolica di alta qualità costruendo uno schema di apprendimento multi-task che coinvolge valutazioni di qualità, riconoscimento delle emozioni e riconoscimento dei generi. Inoltre, abbiamo creato XMIDI, un dataset di musica simbolica su larga scala che contiene 108.023 file MIDI annotati con precise etichette di emozioni e generi. Valutazioni oggettive e soggettive mostrano che XMusic supera significativamente i metodi attuali più avanzati con un'ottima qualità musicale. Il nostro XMusic è stato premiato come uno dei nove Highlights di Collectibles al WAIC 2023. La homepage del progetto XMusic è https://xmusic-project.github.io.
English
In recent years, remarkable advancements in artificial intelligence-generated content (AIGC) have been achieved in the fields of image synthesis and text generation, generating content comparable to that produced by humans. However, the quality of AI-generated music has not yet reached this standard, primarily due to the challenge of effectively controlling musical emotions and ensuring high-quality outputs. This paper presents a generalized symbolic music generation framework, XMusic, which supports flexible prompts (i.e., images, videos, texts, tags, and humming) to generate emotionally controllable and high-quality symbolic music. XMusic consists of two core components, XProjector and XComposer. XProjector parses the prompts of various modalities into symbolic music elements (i.e., emotions, genres, rhythms and notes) within the projection space to generate matching music. XComposer contains a Generator and a Selector. The Generator generates emotionally controllable and melodious music based on our innovative symbolic music representation, whereas the Selector identifies high-quality symbolic music by constructing a multi-task learning scheme involving quality assessment, emotion recognition, and genre recognition tasks. In addition, we build XMIDI, a large-scale symbolic music dataset that contains 108,023 MIDI files annotated with precise emotion and genre labels. Objective and subjective evaluations show that XMusic significantly outperforms the current state-of-the-art methods with impressive music quality. Our XMusic has been awarded as one of the nine Highlights of Collectibles at WAIC 2023. The project homepage of XMusic is https://xmusic-project.github.io.

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