Improving Controllability and Editability for Pretrained Text-to-Music Generation Models | Paper Citation Record

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This paper cites MusicLM: Generating Music From Text.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models MusicLM: Generating Music From Text

Reference 1

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Observation d34cade4-29f9-4155-a4e3-af00fe1ce649 · outbound

This paper cites Look, listen, and learn more: Design choices for deep audio embeddings.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Look, listen, and learn more: Design choices for deep audio embeddings

Reference 6

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Observation c1dbbc6d-6d7c-4958-b17f-ec351a2cf76e · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Observation e601f507-1b25-4895-bf77-6822b18f1367 · outbound

This paper cites LP-MusicCaps: LLM-Based Pseudo Music Captioning.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models LP-MusicCaps: LLM-Based Pseudo Music Captioning

Reference 10

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Observation 86c8d089-5c58-4df5-955c-7d032858bf5d · outbound

This paper cites SingSong: Generating musical accompaniments from singing.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models SingSong: Generating musical accompaniments from singing

Reference 11

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This paper cites Joint music and language attention models for zero-shot music tagging.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Joint music and language attention models for zero-shot music tagging

Reference 12

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Observation c37ee982-b157-47eb-8672-99a29fa3df47 · outbound

This paper cites Long-form music generation with latent diffusion.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Long-form music generation with latent diffusion

Reference 13

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Observation 214f2509-3365-4596-b4c2-900f898a8b6c · outbound

This paper cites VampNet: Music Generation via Masked Acoustic Token Modeling.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models VampNet: Music Generation via Masked Acoustic Token Modeling

Reference 14

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Observation 506b6021-d676-4bda-b7e1-a3572e26369f · outbound

This paper cites Audio set: An ontology and human-labeled dataset for audio events.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Audio set: An ontology and human-labeled dataset for audio events

Reference 15

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Observation 40bc01b0-0948-4894-8ce8-254c2ef195ae · outbound

This paper cites CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models

Reference 16

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Observation eca96272-26cb-4398-8d2b-0d765900055d · outbound

This paper cites InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models

Reference 17

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Observation de35cce0-8fe5-440a-9b0a-fdbdb3d63b6b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

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Observation 524d5f25-d188-48a5-8ee9-4bfb8847f6c7 · outbound

This paper cites M$^{2}$UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models M$^{2}$UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models

Reference 21

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Unresolved cited work

Reference 22

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Observation b0111806-a9f6-4f14-af0e-a808c2b3e499 · outbound

This paper cites Retrieval Augmented Generation of Symbolic Music with LLMs.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Retrieval Augmented Generation of Symbolic Music with LLMs

Reference 24

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Observation 7959ab72-2e7a-45c1-94b1-1a48bcaa3460 · outbound

This paper cites Fr\'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Fr\'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms

Reference 25

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This paper cites Auto-Encoding Variational Bayes.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Auto-Encoding Variational Bayes

Reference 27

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Observation c664983b-0f43-43a8-8a62-8aaabf079cfa · outbound

This paper cites SMITIN: Self-Monitored Inference-Time INtervention for Generative Music Transformers.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models SMITIN: Self-Monitored Inference-Time INtervention for Generative Music Transformers

Reference 29

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Observation a7f48f30-1d70-4f13-99b9-16df11f82b7d · outbound

This paper cites Efficient Training of Audio Transformers with Patchout.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Efficient Training of Audio Transformers with Patchout

Reference 30

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Observation e13e77da-de29-4894-a1f3-33bcdf016491 · outbound

This paper cites PerTok: Expressive Encoding and Modeling of Symbolic Musical Ideas and Variations.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models PerTok: Expressive Encoding and Modeling of Symbolic Musical Ideas and Variations

Reference 31

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Observation 1f7e1e4f-e84d-4d2f-83ce-0fc66c659fb9 · outbound

This paper cites Content-based Controls For Music Large Language Modeling.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Content-based Controls For Music Large Language Modeling

Reference 32

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This paper cites Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls

Reference 33

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Reference 34

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 35

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Observation d54e9a4e-c430-4d5f-bba0-18693ff386d3 · outbound

This paper cites Novice-AI music co-creation via AI-steering tools for deep generative models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Novice-AI music co-creation via AI-steering tools for deep generative models

Reference 36

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This paper cites MuseCoco: Generating Symbolic Music from Text.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models MuseCoco: Generating Symbolic Music from Text

Reference 37

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This paper cites Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders

Reference 38

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This paper cites The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation

Reference 39

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This paper cites Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity

Reference 40

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This paper cites 2019.8937170.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models 2019.8937170

Reference 41

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This paper cites Stem- gen: A music generation model that listens.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Stem- gen: A music generation model that listens

Reference 44

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This paper cites Zero-shot image-to-image translation.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Zero-shot image-to-image translation

Reference 45

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Glove: Global vectors for word representation

Reference 46

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Deep contextualized word representations

Reference 49

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Generalized multi-source inference for text conditioned music diffusion models

Reference 50

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This paper cites Paguri: a user experience study of creative interaction with text-to-music models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Paguri: a user experience study of creative interaction with text-to-music models

Reference 51

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This paper cites Audio Conditioning for Music Generation via Discrete Bottleneck Features.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Audio Conditioning for Music Generation via Discrete Bottleneck Features

Reference 52

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Unresolved cited work

Reference 53

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This paper cites URL https://doi.org/10.1109/ICASSP.2019.8683855.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models URL https://doi.org/10.1109/ICASSP.2019.8683855

Reference 54

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This paper cites Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation

Reference 55

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This paper cites Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling

Reference 56

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models LLaMA: Open and Efficient Foundation Language Models

Reference 57

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Plug-and-play diffusion features for text-driven image-to-image translation

Reference 58

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models WaveNet: A Generative Model for Raw Audio

Reference 59

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 61

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This paper cites Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task

Reference 62

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This paper cites Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset

Reference 63

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Iteratta: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models

Reference 64

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models UniAudio: An Audio Foundation Model Toward Universal Audio Generation

Reference 65

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models UniAudio: An Audio Foundation Model Toward Universal Audio Generation

Reference 66

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This paper cites JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation

Reference 67

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Observation 7aaae18c-fb86-4487-a150-aefcaa3649a6 · outbound

This paper cites ChatMusician: Understanding and Generating Music Intrinsically with LLM.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models ChatMusician: Understanding and Generating Music Intrinsically with LLM

Reference 68

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This paper cites LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Reference 69

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This paper cites Cosmic: A conversational interface for human-ai music co-creation.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Cosmic: A conversational interface for human-ai music co-creation

Reference 70

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Observation 28009de3-3aab-4a83-b2f9-07d17531f7d8 · outbound

This paper cites Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model

Reference 71

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This paper cites A Survey of Large Language Models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models A Survey of Large Language Models

Reference 72

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Masked Audio Generation using a Single Non-Autoregressive Transformer

Reference 73

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This paper cites Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models

Reference 1986

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This paper cites Exploring XAI for the Arts: Explaining Latent Space in Generative Music.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Exploring XAI for the Arts: Explaining Latent Space in Generative Music

Reference 1996

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models 2003.819861

Reference 2004

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Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Improving Text-To-Audio Models with Synthetic Captions

Reference 2013

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This paper cites MoisesDB: A dataset for source separation beyond 4-stems.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models MoisesDB: A dataset for source separation beyond 4-stems

Reference 2014

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Observation b6bd2d2e-5f02-4445-ac86-2279b1482464 · outbound

This paper cites MidiCaps: A large-scale MIDI dataset with text captions.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models MidiCaps: A large-scale MIDI dataset with text captions

Reference 2015

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This paper cites A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions

Reference 2016

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This paper cites Au- diocaps: Generating captions for audios in the wild.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Au- diocaps: Generating captions for audios in the wild

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:22:19.265481Z

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This paper cites MuLan: A Joint Embedding of Music Audio and Natural Language.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models MuLan: A Joint Embedding of Music Audio and Natural Language

Reference 2019

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This paper cites SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition

Reference 2020

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This paper cites Jukebox: A Generative Model for Music.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Jukebox: A Generative Model for Music

Reference 2021

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This paper cites Musicldm: Enhancing novelty in text- to-music generation using beat-synchronous mixup strategies.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Musicldm: Enhancing novelty in text- to-music generation using beat-synchronous mixup strategies

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:22:19.343974Z

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Observation 56994326-27b9-4dee-bca5-b81e86bbbaf9 · outbound

This paper cites Hts-at: A hierarchical token-semantic audio transformer for sound classification and detection.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models Hts-at: A hierarchical token-semantic audio transformer for sound classification and detection

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:22:19.355936Z

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Observation 0a50e756-deba-4490-bf58-6fdf0d6ea5b5 · outbound

This paper cites W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training.

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:22:19.332606Z

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