Total matches
81
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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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76-81
Core papers shown
4
Rows with topics
4
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.
Query surface
Search controls
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.
Workflow map
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 “music information retrieval”
Order: lexical_rank desc, year desc, citation_count desc, work_id ascTotal matches: 81
Resolved filters: scope all_included
- corematched: abstract
Automatic music transcription with note level output is a current task in the field of music information retrieval. In contrast to the piano case with very good results using available large datasets, transcription of non-professional sing...
- current task in the field of [[music]] [[information]] [[retrieval]]. In contrast to the piano case with
Music and Audio ProcessingDiverse Musicological StudiesMusic Technology and Sound Studies - corematched: abstract
The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus...
- corpus constitutes a challenging scenario for [[music]] [[information]] [[retrieval]] tasks such as fundamental frequency estimation, onset
Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing - corematched: abstract
A corpus of 1878 recorded works of historic electronic music from 1950-1999 has been collated. This novel data set empowers chronological study of variation over time, and the answering of research questions based on associated annotated m...
- carried out via the SuperCollider [[Music]] [[Information]] [[Retrieval]] code library, and results of tackling two example
Music and Audio ProcessingDiverse Musicological StudiesMusic Technology and Sound Studies - corematched: abstract
Recent advances in automatic music transcription have facilitated the creation of large databases of symbolic transcriptions of improvised music forms including jazz, where traditional notated scores are not normally available. In conjunct...
- useful in a variety of [[music]] [[information]]-[[retrieval]] tasks, including artist identification and expressive performance modeling
Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing - includedmatched: abstract
Music scores are used to precisely store music pieces for transmission and preservation. To represent and manipulate these complex objects, various formats have been tailored for different use cases. While music notation follows specific r...
- major issue when dealing with [[musical]] [[information]] extraction and [[retrieval]]. We present an automated approach...rhythm/time inconsistencies in the encoding of individual [[musical]] elements, and ii) contextual errors, i.e. notation mistakes
- includedmatched: abstract
Analyzing music similarity in large catalogs is challenging because people perceive music differently and important details are found in audio, text, and metadata. This article introduces a multimodal framework that uses an ontology to mak...
- framework on several open [[music]] and audio datasets using content-based [[retrieval]] tasks and standard ranking...work lays the groundwork for explainable, cognitively [[informed]] [[music]] recommendation systems and points to future work
Showing 76-81 of 81