The editor of Downcodes reports: At the just-concluded 2024 International Conference on Music Information Retrieval (ISMIR), an eye-catching research result-MusiConGen model was officially unveiled. This model uses the Transformer architecture and introduces a time condition mechanism to achieve precise control of rhythm and chords in the field of music generation. The music samples it generates cover a variety of styles and show excellent accuracy and style consistency. This technological breakthrough brings new possibilities to the fields of music creation and artificial intelligence music generation. Let’s take a closer look at this exciting development.
At the 2024 International Conference on Music Information Retrieval (ISMIR), researchers demonstrated their newly developed MusiConGen model. This model is a text-generated music model based on Transformer. By introducing a time condition mechanism, it significantly improves the ability to control music rhythm and chords.
Product entrance: https://top.aibase.com/tool/musicongen
The MusiConGen model is fine-tuned based on the pre-trained MusicGen-melody framework and is mainly used to generate music clips of various styles. By setting control parameters for chords and rhythm, the research team demonstrated the music samples generated by the model, covering five different styles: casual blues, smooth acid jazz, classic rock, high-energy funk, and heavy metal.
Each style of music has clear chord and rhythm requirements. These data are derived from the RWC-pop-100 database, and the generated chords are estimated by the BTC chord recognition model.
To verify the effectiveness of MusiConGen, the researchers compared it with the baseline model and the fine-tuned baseline model. With the same chord and rhythm control settings, MusiConGen demonstrated higher accuracy and style consistency in the generated music samples, reflecting its technical advantages in music generation.
Highlight:
? MusiConGen is a text-generated music model based on Transformer that can enhance control of rhythm and chords through time conditions.
By comparing with traditional models and fine-tuned models, MusiConGen demonstrated significant improvements in music generation.
? The music generated by the model covers five different styles and can accurately simulate specific chord and rhythm requirements.
The emergence of the MusiConGen model marks another major breakthrough of artificial intelligence in the field of music generation, providing new tools and possibilities for music creation. Its precise rhythm and chord control capabilities, as well as the potential for multi-style music generation, bring unlimited imagination to future music creation. We look forward to MusiConGen bringing more amazing music works in the future!