Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation. Despite their versatility, they require estimating the joint distribution over the sources, necessitating pre-separated musical data, which is rarely available, and fixing the number and type of sources at training time. This paper generalizes MSDM to arbitrary time-domain diffusion models conditioned on text embeddings. These models do not require separated data as they are trained on mixtures, can parameterize an arbitrary number of sources, and allow for rich semantic control. We propose an inference procedure enabling the coherent generation of sources and accompaniments. Additionally, we adapt the Dirac separator of MSDM to perform source separation. We experiment with diffusion models trained on Slakh2100 and MTG-Jamendo, showcasing competitive generation and separation results in a relaxed data setting.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models
Pre-trained Language Models (PTLMs) have been shown to perform well on natural language reasoning tasks requiring commonsense. Prior work has leveraged structured commonsense present in knowledge graphs (KGs) to assist P…
ARCKnowledge GraphsNovel ConceptsSentenceCG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection
In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a…
Conditional Text GenerationIntent DetectionLanguage ModelingLanguage Modelling+3QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, chang…
Response GenerationGeneralized Domain Conditioned Adaptation Network
Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision. Recent advances in DA mainly pr…
AttributeDomain AdaptationGeneralized Zero-shot ICD Coding
The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is in high demand as the manual coding can be labor-intensive and error-prone. It is a multi-la…
ClassificationGeneral ClassificationGeneralized Zero-Shot LearningMulti Label Text Classification+4