Signal processing extracts usable information from raw data for mathematical models. The practice appears across engineering domains including imaging, audio, speech, and radar systems. Applications range from noise filtering to object reconstruction.
The mammalian brain demonstrates signal processing naturally: "The primary visual cortex in the mammalian brain acts as a signal processor, taking image data from the retina and performing edge and line detection."
Traditional Approaches
Conventional signal processing employs well-understood mathematical models: regression, decision trees, and support vector machines. Signal processing is defined broadly as adapting and augmenting input data to improve model performance, excluding internal model modifications like fine-tuning techniques such as LORA.
Applications in AI
Prior work covers image and video processing for CNNs, referencing Klein bottles and Klein CNN research. Z. Hu's team applied similar techniques to speech analysis by converting speech signals into spectrograms.
Language Model Signal Processing
For large language models, BluelightAI developed features using parts-of-speech tagging. These features associate each word with its grammatical role, creating embeddings from a smaller vocabulary than natural language. The approach demonstrated "significant improvement in perplexity in a small language model."
Key Advantages
- Applicable to both fine-tuning and ab initio training
- Introduces a priori knowledge, enabling more interpretable models
- Enables systematic feature combination ("LEGO blocks") for domain adaptation
- Produces more generalizable models