Total matches
13
Detect emerging, bridge-candidate, and undercited papers inside a curated audio-ML corpus, then expose the signals behind every recommendation.
Search
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.
Total matches
13
Visible window
1-13
Core papers shown
7
Rows with topics
6
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
title + abstract.Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Workflow map
01
Start with lexical retrieval and narrow with year, venue, topic, and scope filters until the candidate set is focused enough to inspect.
02
Open the paper dossier to review abstract, metadata, ranking presence, and adjacent papers without losing the corpus framing.
03
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
Resolved filters: scope all_included
Automatic Music Transcription (AMT) plays a fundamental role in Music Information Retrieval (MIR) by converting raw audio signals into symbolic representations such as MIDI or musical scores. Despite advances in deep learning, accurately t...
To improve the accuracy of automatic piano music transcription in complex environments, a recognition system applicable to practical scenarios such as music education assistance and intelligent performance analysis was developed.First, aud...
Automatic Music Transcription (AMT) for piano is difficult for audio-only systems due to dense polyphony, resonance, and reverberation, which lead to false positives and unstable onset decisions. We present a multimodal AMT framework that...
Symbolic music datasets are important for music information retrieval and musical analysis. However, there is a lack of large-scale symbolic datasets for classical piano music. In this article, we describe the creation of the GiantMIDI-Pia...
Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription
lex 0.333Automatic Music Transcription (AMT) is usually evaluated using low-level criteria, typically by counting the number of errors, with equal weighting. Yet, some errors (e.g. out-of-key notes) are more salient than others. In this study, we d...
Recent advances in automatic piano transcription have enabled large scale analysis of piano music in the symbolic domain. However, the research has largely focused on classical piano music. We present PiJAMA (Piano Jazz with Automatic MIDI...
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...
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...
La transcription musicale est la procédure consistant à transformer une performance musicale en une partition. Transcrire manuellement un enregistrement audio en écriture musicale est cependant une tâche particulièrement chronophage et ard...
Automatic Music Transcription (AMT)-the task of converting music audio into note representations - has seen rapid progress, driven largely by deep learning systems. Due to the limited availability of richly annotated music datasets, much o...
Symbolic music datasets with matched scores and performances are essential for many music information retrieval (MIR) tasks. Yet, existing resources often cover a narrow range of composers, lack performance variety, omit note-level alignme...
A highly effective music synthesizer should deliver high-fidelity audio for a mix of instruments and voices. Current synthesizers often need to choose between specialized models that provide detailed control over specific instruments and f...
This paper introduces the Beethoven Piano Sonata Dataset (BPSD), a multi-version dataset focusing on the first movements of Beethoven's 32 piano sonatas. Recognized as pivotal works in classical music, Beethoven's piano sonatas have profou...