Matteo Spanio, Antonio Rodà
We lifted 13 functions out of this paper's own repositories and ran 12 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| CSCPadova/wav2taste | canonical | 12 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| cache_dir | Ran | CSCPadova/wav2taste/src/wav2taste/fma_cache.py code served (permissive licence) · get_code("895d6ee9ba51f5a7") |
| cache_exists | Ran | CSCPadova/wav2taste/src/wav2taste/cache.py code served (permissive licence) · get_code("c833176460f985a0") |
| cache_paths | Ran | CSCPadova/wav2taste/src/wav2taste/cache.py code served (permissive licence) · get_code("7273492675795983") |
| collate | Ran | CSCPadova/wav2taste/src/wav2taste/data.py code served (permissive licence) · get_code("fbe13a6c7e063e66") |
| iter_worklist | Ran | CSCPadova/wav2taste/src/wav2taste/extract.py code served (permissive licence) · get_code("e2e1dccbe11d94bd") |
| layered_cache_exists | Ran | CSCPadova/wav2taste/src/wav2taste/cache.py code served (permissive licence) · get_code("976638ceb6fdbdaf") |
| linear_cka | Ran | CSCPadova/wav2taste/src/wav2taste/cka.py code served (permissive licence) · get_code("f8e3dded91f57f34") |
| load_genre_title_to_top_level | Ran | CSCPadova/wav2taste/src/wav2taste/fma.py code served (permissive licence) · get_code("01df71e9472323b6") |
| pairwise_cka | Ran | CSCPadova/wav2taste/src/wav2taste/cka.py code served (permissive licence) · get_code("4bf722e4e83ebff7") |
| resolve_genre_top_level | Ran | CSCPadova/wav2taste/src/wav2taste/fma.py code served (permissive licence) · get_code("b668d890ae746ef1") |
| shard_dir | Ran | CSCPadova/wav2taste/src/wav2taste/fma_cache.py code served (permissive licence) · get_code("edd802873ffa0ac2") |
| shard_path | Ran | CSCPadova/wav2taste/src/wav2taste/fma_cache.py code served (permissive licence) · get_code("2dc48457341d6fbf") |
| load_manifest | Not yet run | CSCPadova/wav2taste/src/wav2taste/extract.py code served (permissive licence) · get_code("eb311ca4dcb7ed54") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimedia retrieval. We formalise taste-from-audio prediction as a content-based music information retrieval benchmark over a perceptually validated multi-source corpus, comparing ten frozen audio encoders from the four HEAR families under a shared multi-task regression head, with gated late-fusion as a configurable variant. In order to assess the effectiveness of the models, we compute absolute error and rank correlation. The strongest systems predict the five tastes within a macro RMSE of 0.134; on held-out real music their error is less than half a single rater's deviation from the consensus (RMSE 0.13 vs. 0.28), so the model tracks the group consensus more closely than an average human rater, and well below the previous state of the art baseline (0.219). On absolute error the encoders are statistically flat, with a single VGGISH matching the best fusion, but gated late-fusion's advantage is confined to rank correlation (macro Pearson r 0.724 vs. 0.666). Operationalised as a content-based retrieval index, the predicted taste space ranks a 309-item pool far more faithfully than a CLAP-text baseline, which sits at chance; ridge probes and an audio-bandstop knockout read the strongest representations against documented sound-taste correspondences.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2607.03296")
get_code_for_paper("2607.03296")
have("2607.03296")
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