REVIEW 3 cited by
Helicality: An Isomap-based Measure of Octave Equivalence in Audio Data
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Octave equivalence serves as domain-knowledge in MIR systems, including chromagram, spiral convolutional networks, and harmonic CQT. Prior work has applied the Isomap manifold learning algorithm to unlabeled audio data to embed frequency sub-bands in 3-D space where the Euclidean distances are inversely proportional to the strength of their Pearson correlations. However, discovering octave equivalence via Isomap requires visual inspection and is not scalable. To address this problem, we define "helicality" as the goodness of fit of the 3-D Isomap embedding to a Shepherd-Risset helix. Our method is unsupervised and uses a custom Frank-Wolfe algorithm to minimize a least-squares objective inside a convex hull. Numerical experiments indicate that isolated musical notes have a higher helicality than speech, followed by drum hits.
Forward citations
Cited by 3 Pith papers
-
When Does Trial-Real-World Data Fusion Improve Precision? Model Auditing and Selection-Aware Inference for Adaptive-TMLE
Under A-TMLE, RCT+RWD efficiency gain is driven by bias magnitude not complexity, crosses break-even near one residual SD of bias, erodes with sample size, and only a block jackknife gives honest intervals for the gain.
-
Reframing Population-Adjusted Indirect Comparisons as a Transportability Problem: An Estimand-Based Perspective and Implications for Health Technology Assessment
Marginal odds ratios and hazard ratios from MAIC/STC are population-specific even under the shared effect modifier assumption, so applying them to a decision-relevant population is an implicit transport step needing e...
-
Enough?
RCTs are statistically special because they set the treatment probability, but they are not scientifically sufficient because generalization across people, places, treatments, and times requires knowledge we rarely have.
Discussion (0). Continue with ORCID to comment.