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Music ControlNet: Multiple Time-varying Controls for Music Generation

Shih-Lun Wu, Chris Donahue, Shinji Watanabe, Nicholas J. Bryan

arXiv:2311.07069v1cs.SDeess.AS

TL;DR

Text-based music generation offers broad stylistic control but limited precision over time-varying musical attributes. Music ControlNet adds composable melody, dynamics, and rhythm controls to a diffusion-based generator, including partially specified inputs. It generates realistic music aligned with both extracted and creator-oriented controls and is 49% more faithful to melodies than MusicGen despite substantially fewer parameters and less training data.

  • Problem

    Text-to-music models control global style effectively but are less suitable for precise, time-varying attributes such as melody, dynamics, and rhythm.

  • Method

    Music ControlNet adapts diffusion-based spatial-control methods to combine text with composable melody, dynamics, and rhythm controls, including partially specified time spans.

  • Results

    The model generates realistic music corresponding to extracted and creator-oriented controls and is 49% more faithful to input melodies than MusicGen.

  • Takeaways & Limitations

    Music ControlNet enables creators to combine global textual style with precise, multiple time-varying musical controls and musically improvise in unspecified spans.

Abstract

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Text-to-music generation models are now capable of generating high-quality music audio in broad styles. However, text control is primarily suitable for the manipulation of global musical attributes like genre, mood, and tempo, and is less suitable for precise control over time-varying attributes such as the positions of beats in time or the changing dynamics of the music. We propose Music ControlNet, a diffusion-based music generation model that offers multiple precise, time-varying controls over generated audio. To imbue text-to-music models with time-varying control, we propose an approach analogous to pixel-wise control of the image-domain ControlNet method. Specifically, we extract controls from training audio yielding paired data, and fine-tune a diffusion-based conditional generative model over audio spectrograms given melody, dynamics, and rhythm controls. While the image-domain Uni-ControlNet method already allows generation with any subset of controls, we devise a new strategy to allow creators to input controls that are only partially specified in time. We evaluate both on controls extracted from audio and controls we expect creators to provide, demonstrating that we can generate realistic music that corresponds to control inputs in both settings. While few comparable music generation models exist, we benchmark against MusicGen, a recent model that accepts text and melody input, and show that our model generates music that is 49% more faithful to input melodies despite having 35x fewer parameters, training on 11x less data, and enabling two additional forms of time-varying control. Sound examples can be found at https://MusicControlNet.github.io/web/.

I. INTRODUCTION

Music ControlNet addresses the imprecision of text-only music control by combining global style text with composable, time-varying musical controls. It supports fully or partially specified controls and generates realistic music that follows both extracted and creator-oriented inputs.

  • I. INTRODUCTION: Text-to-music systems capture global style attributes but struggle with precise notes and rhythms, while symbolic controls can require composing the entire piece beforehand.These limitations motivate a control interface that preserves text flexibility while adding precise temporal guidance.
  • I. INTRODUCTION: Music ControlNet combines global genre and mood text with composable, time-varying melody, dynamics, and rhythm controls.The framework adapts diffusion-based spatial-control ideas to text-to-audio generation.
  • I. INTRODUCTION: The model supports controls that are fully or partially specified in time, allowing musical improvisation in unspecified spans.This extends composable control beyond requiring users to specify an entire piece.
  • I. INTRODUCTION: The framework generalizes from controls extracted from training-like songs to controls anticipated for creator co-creation.Experiments report realistic music corresponding to control inputs in both settings.

A. Diffusion Models

The paper uses denoising diffusion probabilistic models to generate music data by reversing a progressive noising process. Training learns noise recovery, while inference repeatedly denoises Gaussian noise into a realistic sample.

  • A. Diffusion Models: DDPMs generate data by reversing a Markov process that transforms Gaussian noise into progressively less noisy intermediate representations.The model represents the desired data and intermediate latent variables through a joint distribution.
  • A. Diffusion Models: Training progressively corrupts clean examples with a forward Markov chain whose noise levels are defined by β1, . . . , βM.The noise schedule determines the corruption level at each step.
  • A. Diffusion Models: A neural network is trained with mean squared error to recover the noise added to data at a chosen diffusion level.This noise-prediction objective optimizes a variational lower bound on data likelihood.
  • A. Diffusion Models: At inference, M denoising iterations transform x(M) sampled from N(0, I) into a realistic x(0).DDIM can reduce inference to roughly 50–100 sampling steps instead of a typical 1000, with minimal impact on quality.

B. UNet Architecture for Image Diffusion Models

Image diffusion models use UNet-based denoisers conditioned on text, and ControlNet extends them with pixel-level controls. The resulting model represents outputs conditioned on text and arbitrary control sets.

  • B. UNet Architecture for Image Diffusion Models: UNet diffusion models encode and decode image-like feature maps through progressively downsampling and upsampling halves connected by skip connections.Text conditioning is incorporated into hidden layers through embeddings and/or cross-attention.
  • B. UNet Architecture for Image Diffusion Models: Classifier-free guidance trains conditional and unconditional models together by randomly replacing conditioning information with a null value.Inference then trades off conditioning strength, mode coverage, and sample quality.
  • B. UNet Architecture for Image Diffusion Models: ControlNet adds pixel-level controls to pretrained text-to-image diffusion models while preserving the text-conditioned backbone.Its controls correspond to attributes asserted at matching output-image pixels.
  • B. UNet Architecture for Image Diffusion Models: The controlled denoiser maps noisy inputs, diffusion steps, text, and control sets to outputs, modeling pθ(x(0) | ctext, C).C denotes the set of all possible control-signal sets.
  • B. UNet Architecture for Image Diffusion Models: ControlNet improves training efficiency by reusing a frozen pretrained UNet and cloning its encoder into a trainable adaptor branch.Zero-initialized convolution layers introduce control information before adaptor outputs are fed back into the main network.

III. MUSIC CONTROLNET

Music ControlNet models spectrograms conditioned on global text and time-varying controls, then uses a vocoder to produce waveforms. Its formulation adapts image-domain controls to the distinct time-frequency structure of audio.

  • III. MUSIC CONTROLNET: The model targets p(w | ctext, C), combining global genre or mood text with a set of time-varying controls.The dataset limits text conditioning to musical genre and mood tags.
  • III. MUSIC CONTROLNET: Because waveforms have high sampling rates, Music ControlNet uses spectrograms as an intermediary and factorizes p(w, s | ctext, C).The spectrogram has time, frequency-bin, and channel dimensions, making it image-like but audio-specific.
  • III. MUSIC CONTROLNET: The system models pθ(s | ctext, C) with diffusion and applies the DiffWave vocoder to model pϕ(w | s).This separates spectrogram generation from waveform synthesis.
  • III. MUSIC CONTROLNET: Unlike image controls, audio controls align primarily with time and need not share the spectrogram’s frequency dimension.Dynamics can specify intensity over time while leaving the frequency profile for the model to determine.
  • III. MUSIC CONTROLNET: Controls are represented frame-wise with control-specific class dimensions Bn that are not constrained to the spectrogram frequency-bin count B.Controls can be extracted directly from spectrograms, while manually annotated controls are also supported.

B. Adding Time-varying Controls to Diffusion Models

Music ControlNet modifies ControlNet to connect time-varying musical controls with spectrogram frequency structure. Separate control transformations are combined before entering shared zero-convolution processing.

  • B. Adding Time-varying Controls to Diffusion Models: Music ControlNet learns a mapping from input controls to the output spectrogram’s frequency axis, updating image-domain ControlNet for audio.This mapping addresses the mismatch between control representations and spectrogram frequency bins.
  • B. Adding Time-varying Controls to Diffusion Models: The adaptor branch is built from the pretrained text-conditioned UNet encoder and uses newly attached zero-convolution layers.The encoder initialization and zero-convolution structure follow the ControlNet design.
  • B. Adding Time-varying Controls to Diffusion Models: Figure 2 illustrates masking instantiations over the control set, including controls that are masked or retained during training.Masked signals are shown in gray, with N = 3 in the illustrated example.
  • B. Adding Time-varying Controls to Diffusion Models: An additional one-hidden-layer MLP transforms each control’s class dimension B1 to the spectrogram’s frequency-bin dimension B.The MLP simultaneously learns relationships between control classes and frequency bins.
  • B. Adding Time-varying Controls to Diffusion Models: With multiple controls, each control is processed by its own MLP and the transformed outputs are concatenated along the depth dimension.The concatenated representation then enters the shared zero-convolution layer Zin.

C. Masking Strategy to Enable Partially-specified Controls

Music ControlNet trains on randomly masked control subsets and partially masked active controls. This enables inference with arbitrary subsets of controls that may be specified only over selected time intervals.

  • C. Masking Strategy to Enable Partially-specified Controls: Randomly dropping individual control signals during training teaches the model to generate from any subset of the N controls.Keeping or dropping all controls receives higher probability because it produced perceptually better generations.
  • C. Masking Strategy to Enable Partially-specified Controls: For partially specified controls, the method samples start and end timestamps for each active control and masks the control outside the selected interval.The timestamp pair satisfies tn,a < tn,b.
  • C. Masking Strategy to Enable Partially-specified Controls: At each training step, the method uniformly chooses between the two masking schemes after selecting which controls are dropped.Timestamp pairs are sampled only when required by the selected scheme.

D. Musical Control Signals

Music ControlNet uses three complementary time-varying controls—melody, dynamics, and rhythm—that can be extracted from spectrograms without human annotation and created by users at inference time.

  • Control signals: The framework combines melody, dynamics, and rhythm controls that can be extracted directly from target spectrograms without human annotation.These controls also support remixing and creator-authored composition at inference time.
  • Melody: Melody control represents the most prominent musical tone over time using a 12-class chromagram derived frame by frame from spectrogram energy.
  • Dynamics: Dynamics control sums spectrogram energy across frequencies, maps it to decibels, and supports creator-drawn intensity curves over time.The signal also correlates with instrumentation, harmonic texture, and rhythmic density.
  • Rhythm: Rhythm control uses frame-wise beat and downbeat probabilities from an RNN detector, providing time-varying timing information beyond global tempo.
  • Training data: The model is trained on approximately 1,800 hours of licensed instrumental music with genre and mood tags for global style control.

B. Created Controls Dataset Details

The created-controls evaluation uses user-like melody, dynamics, and rhythm inputs, while the generation pipeline combines diffusion models, a compact UNet, and objective controllability and realism metrics.

  • Created controls: Created controls comprise recorded piano melodies, functional dynamics curves, and time-stretched rhythm presets combined with genre and mood tags.The resulting datasets contain 2.0K melody, 2.4K dynamics, and 2.0K rhythm samples.
  • Generation pipeline: The spectrogram generator is a 41-million-parameter convolutional UNet fine-tuned with an adaptor branch for time-varying controls.Pretraining uses 32 A100 GPUs for five days, followed by three days of control fine-tuning on eight GPUs.
  • Generation pipeline: A diffusion-based DiffWave vocoder converts generated spectrograms into audio using six-step fast sampling at 22.05 kHz.
  • Evaluation metrics: Melody accuracy, dynamics correlation, rhythm F1, CLAP, and FAD measure time-varying controllability, text adherence, and audio realism.Dynamics correlation distinguishes within-generation micro correlation from across-generation macro correlation.
  • Evaluation metrics: Rhythm F1 evaluates beat and downbeat timestamp alignment, counting pairs as aligned when they differ by less than 70 milliseconds.

V. EVALUATION AND DISCUSSION

The evaluation tests extracted controls individually and jointly, finding strong time-varying controllability and largely preserved multi-control performance, with mild text-adherence degradation as controls accumulate.

  • Single controls: Single enforced controls substantially improve their corresponding melody accuracy, dynamics correlation, and rhythm F1 over global-style-only generation.This supports effective injection of time-varying controls.
  • Single controls: Dynamics and rhythm metrics can exceed global-style-only results even when their corresponding controls are omitted, likely because the two controls are naturally correlated.
  • Multiple controls: With multiple controls, time-varying controllability remains largely comparable to single-control settings despite the added complexity.
  • Multiple controls: Increasing the number of enforced controls mildly degrades CLAP text adherence, while FAD audio realism is not negatively impacted.

B. From Extracted to Created Controls

Created and partially specified controls extend Music ControlNet beyond extracted training signals, while longer generations retain control and text adherence; against MusicGen, the model is more faithful to melody inputs under a smaller training footprint.

  • From extracted to created controls: Created controls improve all three time-varying controllability metrics across melody, dynamics, and rhythm, demonstrating generalization to out-of-domain inputs.
  • From extracted to created controls: Created controls leave CLAP largely unchanged but degrade FAD, partly because they shift the audio distribution and can produce less musically interesting outputs.The authors note that creators can generate batches and select better samples.
  • Partially specified controls: Partially specified controls cause only mild degradation in time-varying controllability, and controlled and uncontrolled spans do not produce pronounced incoherence.
  • Longer generations: At 12- and 24-second durations, time-varying controllability and text adherence are retained beyond the six-second training duration, while audio realism decreases.

F. Qualitative Analysis of Generations

Music ControlNet follows single, multiple, and partially specified controls while generating cohesive music in controlled and uncontrolled spans. Compared with MusicGen, it provides more precise melody control with far fewer parameters and additional control capabilities.

  • Comparison to MusicGen: Music ControlNet exhibits more precise melody control than MusicGen, especially for created melodies, while using 41M versus 1.5B parameters.The comparison uses MusicCaps inputs and reports comparable text-control adherence under restricted CLAPtag prompts.
  • Comparison to MusicGen: 49% greater melody faithfulness is reported relative to MusicGen, alongside support for multiple controls and partially specified spans.The model additionally supports dynamics and rhythm controls, which MusicGen does not provide in this comparison.
  • Single controls: Single melody, dynamics, and rhythm controls are closely followed for both extracted and creator-created inputs, including some controls outside the training distribution.Creator controls can be simple melodies, drawn dynamics curves, or time-shifted and stretched rhythm templates.
  • Multiple and partial controls: Multiple controls remain composable, and partially specified spans produce cohesive music without visible borders between controlled and uncontrolled regions.The examples include simultaneous melody and dynamics control and combinations of all three controls over selected spans.

B. Time-varying Controls for Music Generation

The paper positions Music ControlNet as a diffusion-based alternative for precise, time-varying music control that complements text-based style conditioning. Its framework supports composable controls, partial temporal specification, and future expansion to richer control inputs.

  • Positioning: Unlike approaches requiring complete musical inputs or style-specific models, the framework combines simpler controls with text control for broad styles.Related MIDI and style-transfer approaches are described as requiring full musical inputs or individual models per style.
  • Framework: Music ControlNet provides a general framework for composable, precise, time-varying musical controls over generated audio.The demonstrated controls are melody, dynamics, and rhythm, which complement one another as basic musical elements.
  • Capabilities and extensions: The framework supports any combination of controls, controls specified only over selected time spans, and creator-envisioned control signals.The paper identifies preset generation from text, speech, or video as a future direction when the number of controls grows.
  • Broader implications: The paper acknowledges that generated music may increase access to musical expression while also creating competitive risks for existing musicians.It frames improved control as a way to offer musicians more creative agency during generation.
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