Primitive Extractor
Overview
The Primitive Extractor is the first trainable module in the WindOne Sound V3 architecture. Its primary function is to compress and refine the high-dimensional audio features extracted by the frozen BEATs model into a lower-dimensional, highly structured "primitive" representation.
Architecture & Flow
- Input Shape:
[Batch, Time, 768](Raw features from BEATs) - Output Shape:
[Batch, Time, 512](Refined primitives) - Total Parameters: ~35 Million
1. Convolutional Stack
The input is transposed and passed through three 1D Convolutional layers with decreasing kernel sizes (7, 5, and 3). Each convolution is followed by Group Normalization (32 groups) and a GELU activation function. This step captures local, short-range temporal patterns and acoustic textures in the audio.
2. Linear Projection
The output of the conv stack (1728 dimensions) is projected down to 512 dimensions using a Linear layer, followed by Layer Normalization. This reduces the dimensionality while preserving the learned temporal features.
3. Self-Attention & Residual Connection
The 512-dimensional features are passed through an 8-head Multi-Head Self-Attention mechanism. This allows the model to capture long-range temporal dependencies across the audio sequence. Finally, a residual connection adds the attention output back to the input, followed by a final Layer Normalization.
Purpose
By transforming the 768-dim BEATs embeddings into 512-dim primitives, this module creates a standardized, dense representation that the downstream Causal Graph Network and Memory modules can efficiently process.