Total matches
81
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
81
Visible window
1-15
Core papers shown
10
Rows with topics
10
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
Over the past two decades, the music information retrieval (MIR) community has grown significantly in both the volume and diversity of research contributions. However, questions remain about who is represented within the community-and who...
Musical instrument classification is a key task in music information retrieval, supporting applications such as automatic transcription and music recommendation. Research on this topic for traditional Persian music has been limited, largel...
This paper is about the story of my relationship, as a contemporary music composer, with computational tools that are situated in the areas of signal processing, machine learning and music information retrieval (MIR). I believe that sharin...
Data are crucial in various computer‑related fields, including music information retrieval (MIR), an interdisciplinary area bridging computer science and music. This paper introduces CCMusic, an open and diverse database comprising multipl...
This article examines ethical dimensions of Music Information Retrieval (MIR) technology. It uses practical ethics (especially computer ethics and engineering ethics) and socio-technical approaches to provide a theoretical basis that can i...
Contrastive learning and equivariant learning are effective methods for self-supervised learning (SSL) for audio content analysis. Yet, their application to music information retrieval (MIR) faces a dilemma: the former is more effective on...
Small adversarial perturbations of input data can drastically change the performance of machine learning systems, thereby challenging their validity. We compare several adversarial attacks targeting an instrument classifier, where for the...
In this article we provide a case study in the datafication of historical handwritten manuscripts, which diversifies the repertoire, approaches, demographics, and institutional partnerships of MIR. The Kiselgof-Makonovetsky Digital Manuscr...
Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity predict...
This editorial introduces the new education track for the Transactions of the International Society for Music Information Retrieval (TISMIR) and aims to provide guidance to both prospective authors and users of this track's material regard...
The regional classification of Turkish folk music remains a relatively unexplored domain in music information retrieval, particularly when leveraging raw audio signals for deep learning. This study addresses this gap by investigating how m...
Citizen science engages volunteers to contribute data to scientific projects, often through visual annotation tasks. Hearing based activities are rare and less well understood. Having high quality annotations of performed music structures...
In music, feature separation is the process of separating distinguishable auditory characteristics, such as pitch, timbre, rhythm, and harmonic content, from a complicated, mixed signal. Virtual reality (VR), gaming, music transcription, k...
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...
Assisting the user in finding music is one of the original motivations that led to the establishment of Music Information Retrieval (MIR) as a research field. This encompasses classic Information Retrieval inspired access to music reposito...