Paper year
2019
Detect emerging, bridge-candidate, and undercited papers inside a curated audio-ML corpus, then expose the signals behind every recommendation.
Paper dossier
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Paper year
2019
Citations
8
Authors
2
Topic labels
3
Source readout
Journal of the Audio Engineering Society
jaes
Core corpus
6
Ranking readout
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
0
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Bridge
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
This paper proposes and evaluates a perceptual model for the measurement of "punch" in musical signals based on a novel algorithm. Punch is an attribute that is often used to characterize music or sound sources that convey a sense of dynamic power or weight to the listener. A methodology is explored that combines signal separation, onset detection, and low level feature measurement to produce a perceptually weighted punch score. The model weightings are derived through a series of listening tests using noise bursts, which investigate the perceptual relevance of the onset time and frequency components of the signal across octave bands. The punch score is determined by a weighted sum of these parameters using coefficients derived through regression analysis. The model outputs are evaluated against subjective scores obtained through a pairwise comparison listening test using a wide variety of musical stimuli and against other computational models. The model output PM95 outperformed the other models showing a "very strong" correlation with punch perception with both Pearson r and Spearman rho coefficients being 0.849 and 0.833 respectively both being significant at the 0.01 level (2-tailed).
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Structural Health Monitoring TechniquesVehicle Noise and Vibration ControlSpeech and Audio Processing
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription
0.601Next handoff
01
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.