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Search the curated corpus with lexical retrieval first.
Title + abstract lexical searchDeterministic ordering
Search v1 is intentionally narrow: lexical retrieval over titles and abstracts, plus practical filters for narrowing the current corpus. Semantic assist can come later, but it is not part of Search v1's scope.
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Search v1 scope: dedicated lexical search, practical filters, and clean handoff into dossier and ranking views. When ranking family filtering is active, the API resolves and returns one explicit run context.
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What Search v1 supports
- Lexical retrieval over
title + abstract. - Filters for year, scope, venue/source, topic label, and ranking family filter.
- Stable ordering: lexical rank, then year, citations, and work id.
- Run metadata appears only when the search depended on ranking state.
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
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How search moves through Research Radar
01
Find the right candidate set
Start with lexical retrieval and narrow with year, venue, topic, and scope filters until the candidate set is focused enough to inspect.
02
Inspect the dossier
Open the paper dossier to review abstract, metadata, ranking presence, and adjacent papers without losing the corpus framing.
03
Move into signals
Use Recommended and Evaluation to understand why a result matters now and how it sits inside the exact resolved ranking run when family filtering is active.
Lexical results
Results for “singing voice”
Order: lexical_rank desc, year desc, citation_count desc, work_id ascTotal matches: 10
Resolved filters: scope all_included
- corematched: title, abstract
This paper introduces GlOttal‑flow LPC Filter (GOLF), a novel method for singing voice synthesis (SVS) that exploits the physical characteristics of the human voice using differentiable digital signal processing. GOLF employs a glottal mod...
- [[Singing]] [[Voice]] Synthesiser
- Filter (GOLF), a novel method for [[singing]] [[voice]] synthesis (SVS) that exploits the physical characteristics...competitive with state‑of‑the‑art [[singing]] [[voice]] vocoders, requiring fewer synthesis parameters and less memory
Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies - includedmatched: title, abstract
Score-based diffusion models have demonstrated promise to separate individual sources from music mixture signals in a generative fashion, paving the way for a new class of solutions for this challenging task. However, existing works rely o...
- [[singing]] [[voice]] separation
- generative modeling to perform weakly-supervised [[singing]] [[voice]] separation for Carnatic Music, a music repertoire
- includedmatched: title
La veu humana és sens dubte l'instrument musical més accessible i possiblement per això part de múltiples pràctiques musicals a tot el món. Aquesta accessibilitat universal porta a una àmplia quantitat d'activitats populars relacionades am...
- [[singing]] [[voice]] transcription
- corematched: abstract
This paper presents a comprehensive study on real‑time music rhythm analysis, covering joint beat and downbeat tracking for diverse kinds of music signals. We introduce BeatNet+, a two‑stage approach to real‑time rhythm analysis built on a...
- models for challenging music scenarios, including isolated [[singing]] [[voices]] and non‑percussive music. Two adaptation strategies...tracking F1 scores for generic music, isolated [[singing]] [[voices]], and non‑percussive audio, with competitive latency
Music and Audio ProcessingMusic Technology and Sound StudiesNeuroscience and Music Perception - includedmatched: abstract
Singing voice generation progresses rapidly, yet evaluating singing quality remains a critical challenge. Human subjective assessment, typically in the form of listening tests, is costly and time consuming, while existing objective metrics...
- [[Singing]] [[voice]] generation progresses rapidly, yet evaluating [[singing]] quality remains a critical challenge. Human subjective assessment
- corematched: abstract
Choral singing is a central part of musical cultures across the world, yet many facets of this widespread form of polyphonic singing are still to be explored. Music information retrieval (MIR) research on choral singing benefits from multi...
- benefits from multitrack recordings of the individual [[singing]] [[voices]]. However, there exist only few publicly available...designed to support MIR research on choral [[singing]]. The dataset includes recordings of an amateur vocal
Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing - includedmatched: abstract
In 1979, with the broadcast of the televised scenic documentary The Legend of the Three Gorges, the featured song Homesickness emerged as a representative work of the period. Its innovative artistic expression sparked widespread discussion...
- vocalist, Li Guyi, pioneered the "semi-[[voiced]] [[singing]] technique," blending ornamental elements from Hunan flower-drum
Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology
lex 0.286corematched: abstractThis paper introduces the Wagner Ring Dataset (WRD), a multi-modal and multi-version resource on the large-scale opera cycle Der Ring des Nibelungen by Richard Wagner. The Ring comprises four music dramas organized into eleven acts and 21...
- time signatures, scenes, and [[singing]] [[voice]] regions (libretto). Moreover, we provide note event annotations
Music and Audio ProcessingMusic Technology and Sound StudiesNeuroscience and Music PerceptionSiamese network contrastive learning model for the objective evaluation of singing sound quality
lex 0.067includedmatched: n/aThis study extracts target singers' voices from mixed audio with background music and noise, addressing the subjectivity, instability and lack of objective standards in traditional evaluation.An innovative SNN-SpEx+ method is proposed, com...
- corematched: abstract
This paper deals with the automatic transcription of four-part, a cappella singing, audio performances. In particular, we exploit an existing, deep-learning based, multiple F0 estimation method and complement it with two neural network arc...
- automatic transcription of four-part, a cappella [[singing]], audio performances. In particular, we exploit an existing...with two neural network architectures for [[voice]] assignment (VA) in order to create a music transcription
Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies