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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
A Case for Reproducibility in MIR: Replication of 'A Highly Robust Audio Fingerprinting System'
lex 0.292corematched: abstractClaims made in many Music Information Retrieval (MIR) publications are hard to verify due to the fact that (i) often only a textual description is made available and code remains unpublished - leaving many implementation issues uncovered;...
- Claims made in many [[Music]] [[Information]] [[Retrieval]] (MIR) publications are hard to verify...many implementation issues uncovered; (ii) copyrights on [[music]] limit the sharing of datasets; and (iii) incentives
Music and Audio ProcessingDigital Humanities and ScholarshipDigital and Traditional Archives Management- includedmatched: abstract
Computational musicology and music information retrieval research on Korean Pansori requires reliable analysis of vocal energy and tempo variation across rhythmic patterns known as jangdan. In this work, a jangdan is treated as a downbeat...
- Computational musicology and [[music]] [[information]] [[retrieval]] research on Korean Pansori requires reliable analysis of vocal energy...downbeat period: analogous to downbeats in Western [[music]], it denotes both a rhythmic pattern type
- includedmatched: abstract
Large-scale symbolic melody datasets are essential for data-driven music information retrieval and generation, yet traditional-style Chinese melodies remain scattered across heterogeneous score formats and image sources. Existing extractio...
- datasets are essential for data-driven [[music]] [[information]] [[retrieval]] and generation, yet traditional-style Chinese melodies...notation-and lack unified handling for numbered [[musical]] notation (Jianpu) and automated quality assurance. We propose
- 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...
- singing are still to be explored. [[Music]] [[information]] [[retrieval]] (MIR) research on choral singing benefits from...multitrack dataset of a cappella choral [[music]] designed to support MIR research on choral singing
Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing - includedmatched: abstract
Music source separation (MSS) focuses on decomposing a mixed audio signal into individual instrumental components and is increasingly relevant for music production, restoration, remixing, education, and music information retrieval. Deep le...
- [[music]] production, restoration, remixing, education, and [[music]] [[information]] [[retrieval]]. Deep learning methods, particularly U-Net architectures...source U-Net model for separating selected [[musical]] instruments from stereo mixtures. The system uses magnitude
- corematched: abstract
Identifying beat positions in music recordings, a central task in Music Information Retrieval (MIR), is commonly referred to as beat tracking. Typically, this involves computing an activation function to reveal frame-wise beat likelihood a...
- [[music]] recordings, a central task in [[Music]] [[Information]] [[Retrieval]] (MIR), is commonly referred to as beat...operate offline, requiring access to the entire [[music]] track for processing. In this article, we introduce
Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies - includedmatched: abstract
A principal objective within contemporary Music Information Retrieval (MIR) research is the development of automated systems for genre classification, especially due to the exponential proliferation of digital audio content on platforms su...
- principal objective within contemporary [[Music]] [[Information]] [[Retrieval]] (MIR) research is the development of automated systems...thereby necessitating scalable and intelligent classification solutions. [[Music]] Genre Classification Using Convolutional Neural Networks presents
- includedmatched: abstract
Evaluating the perceptual quality of AI-generative music remains a challenge in music information retrieval and computational creativity applications. Approaches such as those adopted in the MusicEval and AudioMOS challenges primarily rely...
- generative [[music]] remains a challenge in [[music]] [[information]] [[retrieval]] and computational creativity applications. Approaches such...text alignment, it struggles to capture finegrained [[musical]] attributes such as timbral richness, rhythmic precision
- includedmatched: abstract
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...
- conditions), in this work we investigate the [[musical]] dimension-specifically, variations in genre, dynamics, and polyphony...corpus using both traditional [[information]]-[[retrieval]] and [[musically]] [[informed]] performance metrics. Our extensive evaluation isolates
Application of Long Short-Term Memory Intelligent Algorithm in Automatic Classification System
lex 0.289includedmatched: abstractThis research focuses on music genre classification (MGC) and music genre recognition within the field of music information retrieval. Specifically, an MGC system is devised leveraging long short-term memory (LSTM) and recurrent neural net...
- genre recognition within the field of [[music]] [[information]] [[retrieval]]. Specifically, an MGC system is devised leveraging...continuous frame feature representations and assimilating statistical [[information]] from each segment. The proposed model is evaluated
- corematched: abstract
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...
- performances are essential for many [[music]] [[information]] [[retrieval]] (MIR) tasks. Yet, existing resources often cover...composers, totaling 21,763 h of performed [[music]]. PianoCoRe is released in tiered subsets to support
- includedmatched: abstract
It may be argued that music genre classification (MGC) is one of the most important tasks in music information retrieval; however, it still suffers from being a high-dimensional, highly variable, and noisy audio signal. Most traditional de...
- most important tasks in [[music]] [[information]] [[retrieval]]; however, it still suffers from being a high-dimensional...lightweight and noise-resilient solution for scalable [[music]] classification
- includedmatched: abstract
Audio pretrained models are widely employed to solve various tasks in speech processing, sound event detection, or music information retrieval. However, the representations learned by these models are unclear, and their analysis mainly res...
- speech processing, sound event detection, or [[music]] [[information]] [[retrieval]]. However, the representations learned by these models
- includedmatched: abstract
The Greater Caribbean manatee faces significant conservation challenges due to a lack of demographic data in low-visibility habitats. To address this, we present a refined automated manatee counting method pipeline integrating deep learnin...
- utilized k-means clustering on prioritized [[music]] [[information]] [[retrieval]] descriptors-spectral bandwidth, centroid, and roll
- corematched: abstract
Music question-answering (MQA) is a machine learning task where a computational system analyzes and answers questions about music‑related data. Traditional methods prioritize audio, overlooking visual and embodied aspects crucial to music...
- domain expertise to advance MQA and [[music]] [[information]] [[retrieval]]
Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies
Showing 46-60 of 81