Limitations & Biases
Limitations & Biases
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Pic generated by AI
Limitations
Limitations
MusicGen is not able to generate realistic vocals
MusicGen has only been trained in English, it has not performed well in other languages.
MusicGen does not perform equally well for all music styles and cultures (See "Joy").
MusicGen displays awkard ends to songs, like collapsing to silence (See "Wonder").
At times it can be difficult to assess what types of text descriptions provide the best generations (See "Sorrow").
Bias
Bias
Bias in AI systems often stems from the datasets used for training these models. If the dataset contains a disproportionate representation of certain music genres, styles, or cultural backgrounds, the AI could replicate these imbalances in its outputs.
Data Diversity
Performance Variability
Reflecting Training Biases
Potential Biases
Testing on MusicGen revealed noticeable biases. When inputting rare terms, the system chose to ignore them at times. This can lead to instances of cluttered audio. This phenomenon reflects potential limitations in the diversity and breadth of training data for AI music generation tools. To mitigate these biases, developers should focus on expanding and diversifying the training datasets to ensure the AI system can better understand and process a wide range of vocabulary and styles, thereby improving the quality and accuracy of the generated pieces.
In this example, descriptions that are more humanized (or visualized) prove challenging for the model to understand. Consequently, in the first audio piece under the theme "Wonder," only the initial seconds contain melody, while the remainder is ambient sound generated from keywords like "space" and "vastness." It's only in the second prompt, where specific instruments and a sense of space are clearly mentioned, that the AI generates a coherent piece of audio. This illustrates the importance of precise and detailed prompts in guiding AI to produce meaningful and melodious outputs.
The source of data is most likely lacking equal representation of all music cultures.Â
MusicGen has achieved a level of maturity in music generation but still requires ongoing updates and iterations. It's evident that MusicGen is capable of generating more harmonious music when specific instruments and some musical knowledge are provided. Conversely, if descriptions are too impressionistic or rely heavily on visual, humanized narratives, the AI struggles to fully comprehend. This is partly because humans often use adjectives with multiple meanings, and the AI's understanding is limited to the definitions programmed by developers. Currently, it remains challenging for AI to fully grasp the nuances of human emotions. This limitation underscores the need for continued development in AI's ability to interpret and express complex emotional and conceptual inputs more accurately.
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