Unsupervised Musical Object Discovery from Audio
Current object-centric learning models such as the popular SlotAttention architecture allow for unsupervised visual scene decomposition. Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition. Since concepts of opacity and occlusion in vision have no auditory analogues, the softmax normalization of alpha masks in the decoders of visual object-centric models is not well-suited for decomposing audio objects. MusicSlots overcomes this problem. We introduce a spectrogram-based multi-object music dataset tailored to evaluate object-centric learning on western tonal music. MusicSlots achieves good performance on unsupervised note discovery and outperforms several established baselines on supervised note property prediction tasks.
Code (1)
Tasks
ObjectObject DiscoveryProperty PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning Transposition-Invariant Interval Features from Symbolic Music and Audio
Many music theoretical constructs (such as scale types, modes, cadences, and chord types) are defined in terms of pitch intervals---relative distances between pitches. Therefore, when computer models are employed in musi…
Predominant Musical Instrument Classification based on Spectral Features
This work aims to examine one of the cornerstone problems of Musical Instrument Retrieval (MIR), in particular, instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen for th…
ClassificationClusteringGeneral ClassificationInstrument Recognition+2Learning Normal Patterns in Musical Loops
This paper introduces an unsupervised framework for detecting audio patterns in musical samples (loops) through anomaly detection techniques, addressing challenges in music information retrieval (MIR). Existing methods a…
Anomaly DetectionInformation RetrievalMusic Information RetrievalUnsupervised Anomaly DetectionAddressing Emotion Bias in Music Emotion Recognition and Generation with Frechet Audio Distance
The complex nature of musical emotion introduces inherent bias in both recognition and generation, particularly when relying on a single audio encoder, emotion classifier, or evaluation metric. In this work, we conduct a…
Emotion RecognitionFADMusic Emotion RecognitionMusic GenerationGenerating Sample-Based Musical Instruments Using Neural Audio Codec Language Models
In this paper, we propose and investigate the use of neural audio codec language models for the automatic generation of sample-based musical instruments based on text or reference audio prompts. Our approach extends a ge…