MusicWeaver: Editable Long-Range Music Generation

• MusicWeaver is a music generation model that constructs coherent, long-range musical structures using a beat-aligned structural plan.
• It employs a two-stage pipeline where a Structural Planner creates an editable intermediate representation that guides a diffusion-based U-Net renderer.
• Experimental results demonstrate state-of-the-art fidelity and controllability, achieving high structure coherence and precise localized edits.

Searching arXiv for the cited papers to ground the article and confirm metadata. to=arxiv_search.search 彩神争霸苹果 彩神争霸电脑版='{"^^^^^^^^2query2 Coherent Long-Range and Editable Music Generation from a Beat-Aligned Structural Plan\"","max_results":5,"sort_by":"submittedDate","sort_order":"descending"}' code omitted to=arxiv_search.search 天天爱彩票是json code omitted 红鼎క్షेत्र to=arxiv_search.search 天天中彩票能json code omitted 万亚ೆ to=arxiv_search.search code 人人中彩票json {"^^^^^^^^2query2 Coherent Long-Range and Editable Music Generation from a Beat-Aligned Structural Plan","max_results":5} to=arxiv.search code MusicWeaver is a music generation model conditioned on a beat-aligned structural plan, introduced to address a recurrent limitation of current music generators: they capture local textures but often fail to model long-range structure, leading to off-beat outputs, weak section transitions, and limited editing capability. The system is organized as a two-stage pipeline in which a planner translates prompts into an editable intermediate representation of musical form and compositional cues, and a diffusion-based generator synthesizes music under that plan’s guidance. Within the reported experiments, the model is presented as achieving state-of-the-art fidelity and controllability, while the structural plan functions as an explicit interface for preserving global form and enabling professional, localized edits (&&&^^^^2query2^^^^&&&).

^^^^2ti:\2^^^^. Definition and system-level organization

MusicWeaver consists of a planner and a diffusion-based generator. The planner, also described as a Structural Planner, takes a prompt PRESERVED_PLACEHOLDER_^^^^2query2^^^^ and produces a beat-aligned structural plan PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^. The prompt can be text, audio, video, or their fusion. The generator, described as a Diffusion-Based Music Renderer, consumes $\mathcal{S}$, synthesizes a mel-spectrogram via a U-Net diffusion model, and then converts it to waveform with a vocoder.

The high-level architecture is divided into two sides. On the planning side, multimodal encoders feed a confidence-weighted attention module, which produces a fused embedding $E$. This embedding is used by a Beat-Grid Generator that predicts meter $m$, bar count $T_b$, and a tempo curve, and by a Transformer decoder with causal masking and per-bar heads for Section, Energy, Groove, and Harmony. On the rendering side, a Control Encoder upsamples $\mathcal{S}$ to frame rate and injects it into a 2D-U-Net denoiser by concatenation and FiLM modulation, yielding a mel-spectrogram that is then decoded to the final waveform.

This decomposition explicitly decouples structure from synthesis. A plausible implication is that the system’s controllability is not an auxiliary property layered onto an unconditional renderer, but instead follows from the fact that generation is conditioned on an editable, bar-indexed intermediate.

2. Beat-aligned structural plan

The central representation is the beat-aligned structural plan. Its grid component is

$\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$

where meter $m=(n,d)$ specifies beats per bar $n$ and beat unit PRESERVED_PLACEHOLDER_^^^^2ti:\2query2^^^^, and tempo is defined per bar. For each bar, the plan stores attributes

PRESERVED_PLACEHOLDER_^^^^2ti:\2ti:\2^^^^

where PRESERVED_PLACEHOLDER_^^^^2ti:\22^^^^ is section type, PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^3 is energy or loudness level, PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^4 is groove or rhythm class, and PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^5 is harmony, defined as key plus chord. The full plan is

PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^6

To align the plan with spectrogram frames PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^7, each frame PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^8 is assigned a bar index PRESERVED_PLACEHOLDER_^^^^2ti:\2^^^^9 and a bar-phase

$\mathcal{S}$^^^^2query2^^^^

where $\mathcal{S}$^^^^2ti:\2^^^^ is the downbeat time and $\mathcal{S}$2 is the bar duration. From this alignment, the method forms continuous channels $\mathcal{S}$3, $\mathcal{S}$4, normalized tempo $\mathcal{S}$5, one-hot section, energy, groove, and harmony features, and smoothed boundary indicators. These together yield a control tensor $\mathcal{S}$6 (&&&^^^^2query2^^^^&&&).

The representation is notable because it is both temporally explicit and editable at bar granularity. The plan does not merely summarize style; it specifies form, timing, and compositional cues in a way that can be modified prior to rendering. This suggests that MusicWeaver treats long-range coherence as a planning problem rather than as an emergent property of local acoustic generation.

3. Planner and probabilistic factorization

The planner models

$\mathcal{S}$7

In this factorization, the Beat-Grid Generator predicts meter, $\mathcal{S}$8, and the tempo sequence from the fused embedding $\mathcal{S}$9, while a Transformer Decoder with causal self-attention and relative bar positions autoregressively predicts each $E$^^^^2query2^^^^.

This organization imposes an ordering: global beat-grid variables are inferred before bar-level structural attributes. In practical terms, the planner first establishes the temporal scaffold of the piece and then populates each bar with section, energy, groove, and harmony labels. Because prompts may be text, audio, video, or multimodal fusion, the planner serves as the locus of modality integration, whereas the renderer operates on the resulting structural plan.

The reported design addresses a common failure mode of music generation systems that optimize short-horizon realism without securing bar-level or section-level regularity. Here, section labels, energy contours, groove classes, and harmonic annotations are not post hoc analyses of generated audio; they are pre-rendering conditions.

4. Diffusion-based renderer

The rendering backbone is a 2D-U-Net with three down/up resolutions, residual blocks, and self-attention on coarse scales. Conditioning occurs in two ways. First, the control tensor $E$^^^^2ti:\2^^^^ is tiled along frequency and concatenated to the noisy mel-spectrogram $E$2: $E$3 Second, $E$4 is passed through a small temporal CNN that produces multi-scale control features $E$5, which are then used for channel-wise scale/shift FiLM modulation on U-Net residuals.

The diffusion objective is the standard denoising loss

$E$6

with a cosine $E$7 schedule over $E$8 steps and uniform sampling of $E$9. The forward process is

$m$^^^^2query2^^^^

and the reverse process parameterizes $m$^^^^2ti:\2^^^^ via predicted noise $m$2 (&&&^^^^2query2^^^^&&&).

The conditioning path is therefore not limited to a single injection site. Concatenation exposes the denoiser to frame-aligned control channels directly at the input, while FiLM affects intermediate feature processing across scales. This suggests that plan information is intended to influence both low-level temporal realization and higher-level acoustic organization.

5. Training configuration and data pipeline

The reported training data are VGGSound-Caps and V2M-Caps from $m$3, described as containing automatically captioned music/audio/video pairs. Audio is resampled to 24 kHz, peak normalized to $m$4, and loudness normalized to –^^^^2ti:\2^^^^4 LUFS. Mel-spectrogram extraction uses FFT $m$5, hop $m$6 at approximately $m$7 ms, a Hann window, ^^^^2ti:\22^^^^8 bins, $m$8 Hz, $m$9 kHz, and natural-log magnitudes. Training windows are random bar-aligned snippets of $T_b$^^^^2query2^^^^ bars.

The compute configuration is a single NVIDIA A6^^^^2query2query2query2^^^^ GPU. The optimizer is Adam with default settings. For the planner, the paper specifies a Transformer with $T_b$^^^^2ti:\2^^^^-dim hidden size and standard cross-entropy losses for categorical heads. No additional layer-by-layer architectural breakdown or more granular hyperparameter table is provided in the supplied description.

The emphasis on bar-aligned training windows is consistent with the overall design. Rather than training on arbitrary fixed-length crops, the system uses snippets aligned to the structural unit that also indexes its plan representation. A plausible implication is that the training regime is intended to stabilize the relation between symbolic structure and acoustic realization.

6. Evaluation protocol, metrics, and empirical findings

MusicWeaver introduces two task-specific metrics. The Structure Coherence Score (SCS) evaluates long-range form and timing from raw audio using five sub-scores in $T_b$2: beat–onset coupling $T_b$3, tempo stability $T_b$4, boundary clarity $T_b$5, recurrence strength $T_b$6, and section cohesion $T_b$7. These are combined as

$T_b$8

where $T_b$9 are preset weights.

The Edit Fidelity Score (EFS) measures whether a specified plan edit $\mathcal{S}$^^^^2query2^^^^ occurs in region $\mathcal{S}$^^^^2ti:\2^^^^ and remains localized: $\mathcal{S}$2 The protocol also includes standard fidelity metrics—KL, Inception Score (IS), Fréchet Distance (FD), Fréchet Audio Distance (FAD), Production Complexity (PC), and Production Quality (PQ)—together with alignment metrics using CLAP for text and ImageBind-AV for video. The evaluated tasks are Text-to-Music (T2M), Video-to-Music (V2M), and Text + Video-to-Music (TV2M). Reported baselines include AudioLDM-2-Large, Stable Audio Open, MAGNET-large, AudioX, and prior V2M methods for video (&&&^^^^2query2^^^^&&&).

The quantitative highlights reported in Table ^^^^2ti:\2^^^^ are that, across all tasks, MusicWeaver attains lowest KL/FAD/FD and highest IS/PC/PQ among generative models. For structure, the paper reports an SCS comparison on MusicCaps T2M in which baseline best SCS is approximately $\mathcal{S}$3 and MusicWeaver reaches $\mathcal{S}$4. For editability, MusicWeaver reports EFS values of $\mathcal{S}$5 for T2M, $\mathcal{S}$6 for V2M, and $\mathcal{S}$7 for TV2M. Semantic alignment is reported to match or exceed prior art.

Qualitatively, the paper states that section transitions are sharp and musically sensible, motifs recur on cue through beat-aligned callbacks, and edits such as extending a chorus from 8 to ^^^^2ti:\22^^^^ bars or raising energy on bars 4–8 yield localized, high-fidelity changes without artifacts elsewhere. Within the framing of the paper, these results support the claim that explicit bar-level planning improves both long-range coherence and localized edit control.

7. Editability, scope, and limitations

Editing is plan-based. Users directly modify $\mathcal{S}$8: they can change $\mathcal{S}$9 for section, adjust $\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$^^^^2query2^^^^ for energy, swap groove $\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$^^^^2ti:\2^^^^, or insert new bars. The renderer is described as deterministic given plan and noise seeds, and control dropout is stated to ensure that changes do not “bleed” beyond the edited region. Reported examples include Chorus Extension, in which inserting extra bars labeled “chorus” gives EFS approximately $\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$2–$\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$3; Energy Ramp, in which smoothly raising $\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$4 over a span gives EFS approximately $\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$5–$\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$6; and Groove Swap, in which switching from 4/4 to Latin groove on bars 9–^^^^2ti:\22^^^^ gives EFS approximately $\mathcal{B}=(m,\{\mathrm{BPM}_t\}_{t=1}^{T_b}),$7 (&&&^^^^2query2^^^^&&&).

The limitations reported in the paper are specific. The system is single-track and operates in the mel-spectrogram domain; multi-instrument and multi-track rendering remain future work. The plan vocabulary is compact, and richer descriptors such as dynamics, timbre, and polyphony are identified as possible extensions. The current diffusion backbone is lightweight, and larger backbones are suggested as a route to higher fidelity at increased compute cost.

These limitations constrain the interpretation of the model’s editability. MusicWeaver provides localized editing through manipulations of a compact, beat-aligned control vocabulary rather than through unrestricted symbolic or source-separated multitrack operations. This suggests that its principal contribution lies in making long-range form editable and acoustically realizable within a single-track generative pipeline, rather than in solving the broader problem of fully general music production control.