Papers Music Tagging
“Music Tagging” 태그가 달린 논문 35편 · 필터 해제
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
Recent advances in audio-text large language models (LLMs) have opened new possibilities for music understanding and generation. However, existing benchmarks are limited in scope, often relying on simplified tasks or mul…
Beat TrackingGenre classificationInformation RetrievalInstruction Following+5Semantic-Aware Interpretable Multimodal Music Auto-Tagging
Music auto-tagging is essential for organizing and discovering music in extensive digital libraries. While foundation models achieve exceptional performance in this domain, their outputs often lack interpretability, limi…
Decision MakingMusic Auto-TaggingMusic TaggingM2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Contrastive language-audio pre-training (CLAP) has addressed audio-language tasks such as audio-text retrieval by aligning audio and text in a common feature space. While CLAP addresses general audio-language tasks, its …
Audio captioningAudio ClassificationAudio TaggingAudio to Text Retrieval+13Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned latent space can be further transformed t…
Audio ClassificationAudio TaggingClassificationEnvironmental Sound Classification+9MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization
Recent years have witnessed the success of foundation models pre-trained with self-supervised learning (SSL) in various music informatics understanding tasks, including music tagging, instrument classification, key detec…
Contrastive LearningKey DetectionMusic TaggingQuantization+2PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address this gap, we present PIAST (PIano datas…
Information RetrievalMusic Information RetrievalMusic TaggingRetrieval+1Music Foundation Model as Generic Booster for Music Downstream Tasks
We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation model (MFM) designed to extract hierarch…
Music Source SeparationMusic TaggingMusic TranscriptionTowards Training Music Taggers on Synthetic Data
Most contemporary music tagging systems rely on large volumes of annotated data. As an alternative, we investigate the extent to which synthetically generated music excerpts can improve tagging systems when only small an…
Domain AdaptationMusic TaggingTransfer LearningAn Experimental Comparison Of Multi-view Self-supervised Methods For Music Tagging
Self-supervised learning has emerged as a powerful way to pre-train generalizable machine learning models on large amounts of unlabeled data. It is particularly compelling in the music domain, where obtaining labeled dat…
Contrastive LearningMusic TaggingSelf-Supervised LearningPerceptual Musical Features for Interpretable Audio Tagging
In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets…
Audio TaggingInterpretable Machine LearningMusic TaggingTAGJoint Music and Language Attention Models for Zero-shot Music Tagging
Music tagging is a task to predict the tags of music recordings. However, previous music tagging research primarily focuses on close-set music tagging tasks which can not be generalized to new tags. In this work, we prop…
Audio TaggingDecoderMusic TaggingTowards Contrastive Learning in Music Video Domain
Contrastive learning is a powerful way of learning multimodal representations across various domains such as image-caption retrieval and audio-visual representation learning. In this work, we investigate if these finding…
Contrastive LearningGenre classificationMusic TaggingRepresentation Learning+1Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education
This paper describes the methodology followed and the lessons learned from employing crowdsourcing techniques as part of a homework assignment involving higher education students of computer science. Making use of a plat…
Music TaggingLearnable Front Ends Based on Temporal Modulation for Music Tagging
While end-to-end systems are becoming popular in auditory signal processing including automatic music tagging, models using raw audio as input needs a large amount of data and computational resources without domain knowl…
Keyword SpottingMusic TaggingRhythmTAGMusic-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings
In this paper, we consider a novel research problem: music-to-text synaesthesia. Different from the classical music tagging problem that classifies a music recording into pre-defined categories, music-to-text synaesthesi…
DescriptiveMusic TaggingMuLan: A Joint Embedding of Music Audio and Natural Language
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This paper presents MuLan: a first attempt at a…
Cross-Modal RetrievalMusic TaggingRetrievalTransfer LearningS3T: Self-Supervised Pre-training with Swin Transformer for Music Classification
In this paper, we propose S3T, a self-supervised pre-training method with Swin Transformer for music classification, aiming to learn meaningful music representations from massive easily accessible unlabeled music data. S…
ClassificationData AugmentationGenre classificationMusic Classification+3Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation
The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes processing and retrieving meaningful f…
Domain AdaptationEEGElectroencephalogram (EEG)Emotion Recognition+2Unsupervised Source Separation By Steering Pretrained Music Models
We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generation model is conditioned on an input mixt…
Audio GenerationAudio Source SeparationMusic GenerationMusic Tagging+1Codified audio language modeling learns useful representations for music information retrieval
We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explore representations from Jukebox (Dhariwal…
Emotion RecognitionGenre classificationInformation RetrievalKey Detection+7