Nkumar5/associative-learning-embeddings

Cross-modal associative embeddings (MNIST + AudioMNIST)

Frozen encoder embeddings for a cross-modal (image <-> audio) associative memory experiment. Generated by the associative-learning project; see MANIFEST.json for exact provenance.

Contents

File	Rows	Dim	Description
image_train.npz	60,000	768	ViT pooled embeddings of MNIST train images
image_test.npz	10,000	768	ViT pooled embeddings of MNIST test images
audio_train.npz	24,000	768	AST pooled embeddings of AudioMNIST train clips
audio_test.npz	6,000	768	AST pooled embeddings of AudioMNIST test clips

Each .npz holds embeddings (N, 768) plus metadata:

• label — digit class (0-9)
• key — stable source identifier
• audio files additionally carry speaker_id and gender (0=male, 1=female)

Encoders (frozen, never fine-tuned)

• Images: google/vit-base-patch16-224 (ViT-base)
• Audio: MIT/ast-finetuned-audioset-10-10-0.4593 (AST)

Audio is resampled to 16 kHz; the AST feature extractor computes the log-Mel.

Split

The upstream splits leak speakers, so the split here is rebuilt: speaker-disjoint and gender-balanced. AudioMNIST's shipped test set is only 1/12 female (8%), which cannot support a gender-generalization claim.

Pairing caveat

MNIST and AudioMNIST share a label, never an instance — no image of a "3" was spoken as that same "3". Association here is label-mediated, not instance-level. Pairing is a Cartesian product, so every image of a digit may be paired with every clip of that digit, in both directions.