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What Is the Main Limitation of Feed-Forward Neural Networks in Audio Sequence Generation?
Learn why feed-forward neural networks perform poorly in music and speech generation, especially when long-range temporal dependencies and sequence context matter.
Question
What was a critical weakness of Feed-forward Neural Networks when applied to music and speech generation?
A. They were too complex for the available computing power.
B. They could only process short sequences of notes, not entire melodies.
C. They struggled with capturing long-range temporal dependencies due to their fixed-size input.
D. They required extensive human intervention to generate coherent musical outputs.
Answer
C. They struggled with capturing long-range temporal dependencies due to their fixed-size input.
Explanation
Feed-forward neural networks process input without a built-in memory of earlier time steps, which makes them weak at modeling long sequences in music and speech. Because of that, they have difficulty preserving context across time and capturing structure that depends on events far apart in the sequence.
Option B is partly related, but it is less precise because the deeper issue is not just sequence length on its own. The core limitation is the lack of temporal memory and the resulting failure to model long-range dependencies effectively.