Paper year
2025
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
2025
Citations
3
Authors
2
Topic labels
3
Source readout
Journal of the Audio Engineering Society
jaes
Core corpus
Not available yet
Ranking readout
Ranking details could not be loaded (API 503).
Packet loss concealment (PLC) is vital in preserving audio quality for networked music performances. Although existing PLC techniques primarily target speech transmission, the unique challenges in music signals, such as complex harmonic structures and diverse timbral ranges, have yet to be adequately addressed. This is in part a result of the fact that a satisfactory objective evaluation metric for music PLC methods is missing. As a first foundational step toward this direction, this paper proposes a novel evaluation metric that leverages insights from music psychoacoustics and uses the constant-Q transform to better quantify glitch audibility induced by unconcealed packet loss (i.e., replaced with zeros) compared with existing metrics. The authors conducted extensive subjective listening tests leading to the creation of a publicly available ground truth data set, mapping objective audio features to human assessments of glitch audibility. Results show that the developed metric outperforms other measures (such as mean squared error and mean absolute error) in predicting perceptual impacts, taking a step toward addressing the need for a specialized metric for PLC in the domain of networked music performances. However, further improvements are needed to match human perceptual accuracy, which calls for further research on the development of a reliable perceptually motivated evaluation metric.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music Technology and Sound StudiesSpeech and Audio ProcessingMusic and Audio Processing
Neighbor surface
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No embedding-backed neighbors available for this paper/version yet.
Next 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.