Paper year
2019
Paper dossier
Source Separation for Enabling Dialogue Enhancement in Object-based Broadcast with MPEG-H
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
18
Authors
6
Topic labels
3
Source readout
Source and corpus status
Venue
Journal of the Audio Engineering Society
Source slug
jaes
Corpus placement
Core corpus
Similarity rows
6
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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.
Abstract
Dialogue Enhancement (DE) is one of the most promising applications of user interactivity enabled by object-based audio broadcasting. DE allows personalization of the relative level of dialogue for intelligibility or aesthetic reasons. This paper discusses the implementation of DE in object-based audio transport with MPEG-H, with a special focus on source separation methods enabling DE also for legacy content without original objects available. The userbenefit of DE is assessed using the Adjustment/Satisfaction Test methodology. The test results demonstrate the need for an individually adjustable dialogue level because of highly-varying personal preferences. The test also investigates the subjective quality penalty from using source separation for obtaining the objects. The results show that even an imperfect separation result can successfully enable DE leading to increased end-user satisfaction.
Authors
Neighborhood labels
Topics
3 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Speech and Audio ProcessingAdvanced Data Compression TechniquesAdvanced Adaptive Filtering Techniques
Neighbor surface
Similar papers
6 total neighborsEmbedding v1-title-abstract-1536-cleantext-r3
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
- Speech and Audio ProcessingMusic and Audio ProcessingBlind Source Separation Techniques
- Speech and Audio ProcessingBlind Source Separation TechniquesVehicle Noise and Vibration Control
- Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies
- Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing
- Speech and Audio ProcessingBlind Source Separation TechniquesMusic and Audio Processing
- Music and Audio ProcessingMusic Technology and Sound StudiesAnimal Vocal Communication and Behavior
Next handoff
Best next moves from here
01
Check recommendation families
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
Inspect nearby topics
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Cross-check evaluation baselines
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.