MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
Authors: Fang-Duo Tsai
Shih-Lun Wu
Weijaw Lee
Sheng-Ping Yang
Bo-Rui Chen
Hao-Chung Cheng
Yi-Hsuan Yang
Venue: ICML 2025 poster (2025)
Keywords: Sound
Artificial Intelligence
Machine Learning
Audio and Speech Processing
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: A framework that employs rotary positional embeddings in decoupled cross-attention layers to achieve precise controllability for musical attribute conditioning, as well as audio inpainting and outpainting.

Abstract: We propose MuseControlLite, a lightweight mechanism designed to fine-tune text-to-music generation models for precise conditioning using various time-varying musical attributes and reference audio signals. The key finding is that positional embeddings, which have been seldom used by text-to-music generation models in the conditioner for text conditions, are critical when the condition of interest is a function of time. Using melody control as an example, our experiments show that simply adding rotary positional embeddings to the decoupled cross-attention layers increases control accuracy from 56.6% to 61.1%, while requiring 6.75 times fewer trainable parameters than state-of-the-art fine-tuning mechanisms, using the same pre-trained diffusion Transformer model of Stable Audio Open. We evaluate various forms of musical attribute control, audio inpainting, and audio outpainting, demonstrating improved controllability over MusicGen-Large and Stable Audio Open ControlNet at a significantly lower fine-tuning cost, with only 85M trainable parameters. Source code, model checkpoints, and demo examples are available at: https://MuseControlLite.github.io/web/

MuseControlLite:
Multifunctional Music Generation with Lightweight Conditioners
Fang-Duo Tsai!
Shih-Lun 2
Weijaw Lee"
Sheng-Ping Yang' Yi-Hsuan Yang'
Bo-Rui Chen Hao-Chung Cheng'
Abstract
mechanisms to exert controllability going beyond the simple text prompt have been considered critical. As such, recent months have seen an increasing body of research concerning the addition of fine- fine-grained, time-varying control to text-to- music generation, such as those related musical aspects of chords, rhythm, melody, and dynamics. Exemplars include Music ControlNet (Wu et al., 2024), MusiConGen (Lan et al., 2024), JASCO (Tal et al., 2024), and DITTO (Novack et al., 2024b), to name just few.
We propose MuseControll a lightweight mech- anism designed to fine-tune text-to-music genera- tion models for precise conditioning using var- ious time-varying musical attributes and refer- ence audio signals. The key finding is that posi- tional embeddings, which have been seldom used by text-to-music music generation models in the condi- tioner for text conditions, are critical when the condition of interest is a function of time. Using melody control as an example, our experiments show that simply adding rotary positional em- beddings to the decoupled cross-attention layers increases control accuracy from 56.6% to 61.1%. %, while requiring 6.75 times fewer trainable param- eters than Mate-of-the-art fine-tuning mechanisms, using the same pre-traee pre-trained diffusion Transformer model of Stable Audio Open. We evaluate vari- ous forms of musical attribute control, audio in- painting, and audio outpainting, demonstrating improved controllability over MusicGen-Larre and Stable Audio Open ControlNet at a signifi- cantly lower fine-tuning cost, with only 85M train- able parameters. Source code, model checkpoints, and demo examples are available at: https: / //MuseControllitc github. io/web/.
Despite the exciting progress that has been made, we find two avenues for improvement. First, existing models might be wvrrparaaeeterize. In view of the success of Control- Net (Zhang et al., 2023; Zhao al., 2024) in adding spa- tial control for text-to-image generation, prominent ap- proach has been to use similar idea to fine-tune pre-trained text-to-music models to add conditioners for time-varying conditions. For example, treating mel-spectrograms as im- ages, Music ControlNet (Wu et al., 2024) offers multiple conditions of melody, rhythm and dynamics. Stable Au- dio Open ControlNet (Hou et al., 2025) further improves audio quality with latent diffusion, adapting the original U-Net-based ControlNet encoder to diffusion Transformer architecture (Peebles & Xie, 2023). However, the Con- trolNet approach requires duplicating half of the diffusion model as a trainable copy (Zhang et al., 2023), leading to increased training and inference times. Lighter alternatives for fine-tuning, such as the idea of decoupled cross-atlention presented in IP-adapter (Ye et al., 2023), can be studied.
1. Introduction
Second, while fine- tne-ttnnig a text-to-muse generation model to consider conditions of musical attributes or conditions of reference audio signals (Tsai et al., 2024) have been studied separately, little work has been done to consider both types of conditions at the same time. This capability may enable various creative cases, such as adding melody conditions while performing audio inpainting outpainting.
Text-to-music generation models have recently gained pop- ularity as they hold the promise of empowering everyone to create high-quality and expressive music without much musical training and reduced time cost (Copet et al., 2024). However, for people who desire to be more deeply involved in the creation process itself rather than the final output only,
We propose MuseControlLite,, a lightweight fine-tuning mechanism with significantly fewer trainable parameters for controllable text-to-music generation. The key find- ing is that the decoupled cross-attention mechanism (Ye et al., 2023). when augmented with positional embeddings. achieves superior performance while using nearly order
Naatoonal Taiwan University, Taipei. Taiwan. Massachusetts Institute of Technology, Cambridge, MA, United States. Corre- spondence to: Fang- Tsai <fundwotsai2001 @ gmail.com>.
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
of magnitude fewer trainable parameters than ControlNet- based mechanisms (85M vs. 572M). Built on the simple idea of combining the rotary positional embeddings (RoPE) (Su et al., 2024) with decoupled cross-attention, MuseControl- Lite is a novel framework for controlling time-varying condi- tions. Our implementation introduces only an additional 8% trainable parameters relative to the diffusion Transformer backbone (Evans al., 2024c).
Training-time control with global conditions. One of the most common methods to steer a music generation model is direct fine-tuning, Plitsis et al. (2024) explores personalized techniques from image generation, such as DreamBooth (Ruiz et al., 2023) and textual inversion (Gal et al., 2022). Mustango (Melechovsky et al., 2023), trained with music- focused textual captions, learns to understand instructions about chords, tempo, and key. et al., 2024) fine-tunes MusicGen, enabling music editing with text prompts. MusicGen-style (Rouard et al., 2024) trains MusicGen from scratch, incorporating a text condi- tioner and an audio feature extractor to fuse text and audio during inference.
(Zhang
Unlike our approach, ControlNet-based models (Wu al., 2024; Hou et al., 2025) incorporate positional embed- dings in the self-attention layers of the Transformer blocks. These models process the conditioning input in the la- tent matching the shape of the pre-trained back- bone-and reintroduce it into the frozen U-Net decoder (Wu et al., 2024) or Transformer blocks (Hou et al., 2025) to guide the generation toward the desired output. We find empirically that our approach is more parameter-efficient,
Training-time control with local conditions. For more granular controls, MusicGen (Copet et al., 2024) prepends melody-based conditioning tensor (along with text em- beddings) and trains the entire model from scratch. Sim- ilarly, JASCO (Tal et al., 2024) appends all conditions to the model's 's input across the feature dimension but also re- quires training from scratch- demanding sigmificant compu- tational resources and making it less flexible to add new conditions. MusiConGen (Lan et al., 2024) and Coco- Mulla (Lin al., 2023) fine-tune MusicGen, enabling chord and rhythm conditions without full retraining, Music Con- trolNet (Wu et al., 2024) uses pero-initialized convolution layers and a trainable copy as an adapter for fine-tuning.
Our model supports multi-attribute control similarly to Mu- sic ControlNet (Wu et al., 2024). Moreover, by introducing "audio conditioning," MuseControlLite is able to replicate the reference audio in full resolution while providing par- tial control capability, enabling both audio inpainting and outpainting. Additionally, we adopt multiple classifier-free guidance (Liu et al., 2022; Brooks et al., 2023) to flexibly regulate the strength of the time-varying conditions, prevent- ing the quality degradation that can result from over-fixation.
Inference-time optimization. Training-fres approaches for controllable generation also gain attention for their computa- tional efficiency and adaptability (Levy et al., 2023; Novack et al., 2024b; Kim al., 2024). These methods leverage pre- trained models directly, avoiding additional model training. However, training- free methods often encounter inherent quality limitations compared to fully trained models. In our work, we address similar objectives with MuseControl- Lite, which trained on open-source data and provides functionalities similar to DITTO (Novack al., 2024b).
The main contributions are three-fold:
The first investigation of using positional embeddings for decoupled cross-attention layers in diffusion Trans- formers for controllable text-to-music generation.
The first fine-tuning method that handles both attribute and audio control "'ext+aatriuutcaaudoo) In contrast, existing finetuning methods only take either attribute control ("rext-attributee) (Wu et al., 2024), or audio control ("tettaaddio") ) (Tsai et al., 2024).
Demonstration on the public evaluation benchmark of the Song Describer dataset (Manco al., 2023), showing that MuseControll .ite outperforms existing ControlNet-based approaches (Wu et al., 2024; Hou et al., 2025) in melody control, achieving a 4.5% im- provement in melody accuracy.
3. MuseControlLite
3.1. Diffusion Background
Diffusion models (Ho et al., 2020; Song et al., 2020) operate in two stages: a forward process that progressively corrupts a clean sample Xu over T time steps with noise (forming x XT), and a reverse process that is learned to invert the corruption step by step. Diffusion models are often trained to predict the noise c added at each time step by minimizing mean squared error denoising loss.
2. Related Work
Controllable music generation aims to produce music that aligns with human requirements. On a global scale, controls may include text prompts, tempo (BPM), instrumentation, timbre, or mood. On a more fine-grained or local level, control can be exercised over chords, rhythm, dynamics, melodic lines, and other structural elements, usually as time- varying conditions. We review some existing work below.
While early diffusion models use U-Net-like architectures for denoising, diffusion Transformers (Peebles & Xie, 2023; Xie et al., 2024) emerge as a more effective way to capture long-range dependencies in data through the attention mech- anisms. During training, each noisy sequence x; is passed
2

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
through the Transformer along with time-step embedding to predict 6, Xo or other intermediate variables (Salimans & Ho, 2022), depending on the chosen parameterization. By it- eratively refining x, through this denoising loop, a coherent musical sequence can be reconstructed.
from) W and W respectively. In view that time-varying conditions in the music/audio domain may be treated simi- larly as spatial conditions in the image domain, we propose to employ decoupled cross-attention to music generation.
For the original cross-attention layers handling the text con- dition (i.e., the pink-shaded component in the middle of Figure 1), we leave unchanged:
In our implementation, we use Stable Audio Open (Evans et al., 2024c), an open-source text-to-music generation model based on diffusion Transformer with 24 diffusion blocks. Each block contains both self-attention and cross- attention layers. As will be described later, we modify the cross-attention layers to consider time-varying conditions.
X == Attention (x W Ciext W Ciext W"),
(3)
where Crex denotes the embedding representing the text condition.
3.2. Rotary Position Embedding (ROPE) Background
Transferring the technique from the traditional U-Net, we adapt the decoupled cross-attention layers to fit the diffu- sion Transformer, which calls for additional positional en- codings. Specifically, in MuseControlLite. beyond the text condition Cuex, we introduce to the decoupled layers (i.e., the musical attribute pipeline within the green-shaded com- ponent in Figure 1) an additional musical attribute condition Cattr = {C which is a function of time with N time points, and C, is at the time position n of the sequence Caur. We apply ROPE to the query, key, and value vectors to en- hance the model's positional awareness. We then train the duplicated 4 and 4 to handle Cam::
Absolute positional embeddings (Radford et al., 2019; Clark, 2020) add learned or sinusoidal vector (Vaswani, 2017) to each Transformer token, and relative positional embed- dings (Shaw et al., 2018) incorporate distance offsets be- tween tokens in attention. ROPE (Su et al., 2024) instead rotates query and key vectors by a position-dependen an- gle, embedding both absolute and relative information more directly. This is encapsulated by the following equations:
Kr (Ro. W4 W
(R
W W*x)).
xx),
(1)
21 cos m01
22
sin m0,
122
cos m0,
sin m0,
qm R W Xm
(4)
R R
:
:
k, = R W Cn:
(5)
Id-1
cos m0a/2 cos mm
sin m0ay2 sin mdl 2/2
vn = R W Cn.
(6)
xd
Fa-1
For the decoupled cross-attention, we combine the rotated sequences and calculate the attention:
where Xm and x,, in Eq. (I) are embeddings for tokens at positions m and n coming from the last layer, q and k the resulting query and key vectors, W and W the projection matrices, and R.g, the rotation matrix with
X Attention QQupe: Krupe, V Vrupe).
where Qrope = {qm M JM Krope ] V rope { J. where M is the length of the musical audio se- quence (N is proportional to M but they can be different in general). Finally, following Zhang et al. (2023), we add the cross-attention outputs together and connect them with a zero-initialized (zero-init) 1D convolutional layer ZeNN to eliminate initial noise at the start of training. We re- gard this linear superposition as a correction to the query representation based on the given condition:
θ = (0, 10000 2((-1) E [L.2..... } where 0g for each position index i follows an exponential scaling pat- tern based on the dimension d. Note that in Eq. (2) we drop the subscript of x for simplicity and use xy to indicate the j-th entry (j E [1, 2....,d) d) of the vector.
3.3. Proposed Adapter Design
To prevent arerpparametrization yet still achieve the desired fine-tuning performance, Custom Diffusion (Kumari et al., 2023) demonstrates that fine-tuning only w (for key vec- tors) and W" (for value vectors) can suffice for producing personalized outputs with a given new image condition. IP- Adapter (Ye et al., 2023) further improves this approach by adopting decoupled cross-attention mechanism that trains only 7 and 4 in the "decoupled" layers to learn new conditions related to image generation, where 4 and Wh are learnable copy of (and with parameters initialized
x = ZZNN (Xtext + Xatt)).
(8)
3.4. Applications for Controls and Manipulations
We first demonstrate that our model is applicable to all time- varying signals used in Music ControlNet (Wu et al., 2024). Next, we incorporate an additional audio condition to enable audio inpainting and outpainting, treating audio signals as another type of control signals.
3

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
musical attribute condition pipeline
audio condition pipeline
Rotary positional embedding
Text condition pipeline
Trainable
Foed Forward
init CMN
zero init CNN
Freeze
Layer Norm
Attention
Attention
Attention
Fasture extractor
W+
W
W
Wi
Multi Cross Attemion
aaeer
Concatenate
T5 based Fiject ancoder
Stable Audio
w
WAE
Fats
Multi Head Sell-Atteniio
aaeer Norm
J 1|
Médden-stitee
Text
Audio
DIT transformer layer
Dynamic
Malady
#hythm
Original condition
Additional Adaptars for MuseControll.ie
Figure 1. MuseControlLite uses multiple condition extractors to handle all time-varying controls. The musical attribute offers control over elements such as melody, thythm, and dynamics, whereas the audio condition enables audio inpainting and outpainting. We train the two pipelines separately with two sets of adapters, but users can choose to use either one or both the inference time.
synchronization and rhythmic nuance.
Table We trained both models for 70,000 steps with hatch size of 32 using the melody condition and found that the one without ROPE struggles to learn the new condition.
We use separate ID CNN layers extract features from each condition and expand their channel dimensions to C,/3, where C, is the cross-attention dimension. We then use PyTorch's interpo late function to match the sequence lengths of these features, setting Ninterpolate equal to the query length M. Finally, we concatenate the features along the last dimension, resulting the condition representation Canss E R Momqponn XC. for the decoupled cross-attention input.
FD1
KL|
CLAP1
Mel acc.
w/o ROPE
113.13
0.58
0.41
10.7%
w/ ROPE
78.50
0.29
0.38
58.6%
Musical Attribute Control We follow Music Control-
Net (Wu et al., 2024) extract rhythm and dynamics, but adopt method similar that of Hou et al. (2025) for melody extraction. For melody Cmel E R Nucis, x 128 we first compute the CQT with 128 bins for the mean of the left and right audio channels, apply an argmax operation, then to retain the four most prominent pitches per frame, followed by high-pe gish-passsss filter (cutoff 261.2 Hz) to suppress the bass. Dynamics Cdyn E R Napammix are derived from the spectrogram energy, converted to decibels, and smoothed with Savitzky-Gol sity. Rhythm City E IR Naytio X2 is extracted using recurrent network- beat detector (Böck et al., 2016) that esti- mates beat and downbeat probabilities, enabling precise
During training. we apply a masking strategy similar Mu- sic ControlNet (Wu et al., 2024), which randomly masks 10% to 90% of the condition, and the masks are independent for the three conditions (i.e., melody, dynamics, rhythm). By using such partial conditioning, we find that the model learns "disentangle" these conditions and can improvise for the unconditioned segments. For example, we can spec- ify music attribute conditions only for the 10-20-second segment, leaving the 0-10-second and 20-47-second seg- ments blank for the model to improvise.
filter to align with perceived inten-
Audio Inpainting and Outpainting We directly use the VAE-encoded latent Xo R Neudre X referred to as the "clean latent" for Stabie as Caudio. where Nantio is the length of the encoded audio and A is the number of audio latent channels. Since Candio provides far more information than Caurs we found that training with both Caur and Caudio simultaneously can cause the model to ignore Caur- Thus,
We noticed that Stable Audio Open ControlNet computes the CQT for the left and right channels separately yields finer-graing grained melody condition. Adopting this method in our implementation improved melody accuracy to 64.5% after only 18,000 training steps.

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
the audio condition Caudio is trained separately using a dis- tinct set of adapters, while the decoupled cross-attention layers remain the same as those used in the musical attribute conditioning pipeline. The audio condition is applied with a complementary mask to Catrr so that there is no overlap between Caur and Caudio-
following this pipeline: (i) Resample the audio to 44.1 kHz and segmented into fixed-length clips compatible with the maximum input shape of the Stable Audio Open VAE en- coder: (ii) we employ the sound event detection capabilities of PANNs (Kong et al., 2020) to exclude any audio samples containing vocals; (iii) captions for each audio clip are gener- ated using the Qwen2- Mandio-B--nntruue model (Chu et al., 2024). Additionally, we remove any samples that overlap with the Song Describer dataset (Manco et al., 2023), as this dataset is reserved exclusively for evaluation purposes. We will also release the code? for this data processing pipeline, aiming to facilitate and accelerate future developments in text-to-music model training and fine-tuning. For evalua- tion, we adopt the methodology outlined by Evans et al. (2024a:c;b). Specifically, we utilize the instrumental sub- set of the Song Describer dataset, explicitly excluding any prompts associated with vocals. This yields evaluation set comprising 586 audio clips. All 586 clips are used in the melooyy-condttioned generation experiments described in Section 5.1, aligning with the settings in Stable Audio Open ControlNet (Hou et al., 2025). However, using musi- cal attributes and text captions from the same audio does not reflect real-world use cases, as users may wish generate music conditioned rather than the exact caption of the melody source a pro- cess known as style transfer. To demonstrate the style trans- fer capability of MuseControll Lite, the tasks described in Section 5.2 employ a different setup. Ideally, we would have followed the same approach in Section 5.1, but this was not possible due to the closed-source nature of Stable Audio Open ControlNet (Hou et al., 2025). To enable style transfer evaluation, we split the 586 audio clips into two disjoint subsets and generated samples by pairing text prompts from the first subset with musical attributes extracted from the second, ensuring that the attributes were independent of the ground-truth audio. The generated outputs were then evalu- ated against the first subset as the reference. For the audio inpainting and outpainting tasks in Section 5.3, we randomly selected a smaller subset of 50 clips from the original 586, without applying the style transfer setting. In Section 5.4, to compare with Stable Audio Open ControlNet, which is open-source, we downloaded example outputs from their project website and generated corresponding samples using the same melody condition audio and text prompts as used by other baselines.
By applying masks Caudio during training, W and W learn not only to reflect the condition k, on the same posi- tion qn but also to attend to distant tokens km, shown in Figure 2 in the appendix. This yields smooth transitions at the boundary where the audio condition is given and where it is masked. MuseControll.ite can thus simultaneously achieve partial audio control and music-attribute control. Since the segments controlled by Caudio are more rigid, we propose to use musical attribute conditions to flexibly con- trol the masked audio segments.
3.5. Multiple Classifier-Free Guidance
Classifier-free guidance (Ho & Salimans, 2022) is an inference-time method that trades off sample quality and diversity in diffusion models. It is often used in text- conditional audio or image generation to improve text ad- herence. Song et al. (2020) provides a crucial interpre- tation that each denoising step can be viewed as ascend- ing along Vz px(), the score of po(x). Additionally, any input condition с can be incorporated into a diffu- sion model by injecting the embeddings of y into cross- attention (Rombach et al., 2022), thus modeling pe (x(x)c) (and vr log Po (o (x)).. Ho & Salimans (2022) shows that we can train a model by randomly dropping the condition, thereby learning both V. Joggogpe (x) and Va log px((c). Specifically, this procedure enables V log Px = V. lologpt p(x) + Aveu log p(xcc) Vz V- log In our work, we use a single pipeline model po Catr: Caudio) during training. Our pilot study finds that fine-tuned model often over-fits to the additional conditions Catr or Caudio- Therefore, we adopt a separated guidance scale (Brooks et al., 2023), Aater for musical attributes and Aandio for audio:
melody using arbitrary text prompts,
V, logooxp((x))) V2 logo +
Σ
d V. v logp(x(cg)) - Vz (=logp(((c<))).
(Eetext sudio
As detailed in Appendix B, we expand the equation from single or two conditions (Ho & Salimans, 2022; Brooks et al., 2023) to general form of multiple conditions.
4.2. Training and Inference Specifies
We fine-tune the model using the following objective:
4. Experimental setup
[o fl (a:Xo + t,,,,t) - vell.
(10)
4.1. Dataset
For training, we utilize the open-source JaG-Jddd dataset (Bogdanov et al., 2019) and preprocess the data
rext-to-musi---ttasee
5

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MuseControlLite Multifunctional Music Generation with Lightweight Conditioners
MusicGen (Copet al., 2024): MusicGen is Transformer-basedd autoregressive model for text-to- music generation. We adopt the MusieGen-Stereo- Large for audio inpainting and outpainting, MusicGen- Stereo-Large- Melody for melody comparison.
Table 2. Separated guidance scales used in different tasks
Guidance
Auxt
Aatt
Asudio
Musical attribute control
7.0
2.0
X
Audio inpainting
7.0
2.0
1.0
Audio outpainting
7.0
2.0
1.0
Stable Audio Open ControlNet (Hou et al., 2025):
The ControlNet structure, widely used in text-to-image generation, can also be applied for text-to-musi con- trol. Although Stable Audio Open ControlNet is not open-source (Hou et al., 2025), we employed exactly the same metrics and dataset for comparability. In addition, we contacted the authors to ensure that we followed the same method for extracting melodies and calculating accuracy.
with the velocity term Vt = OgE + 3gx0 following the v-prediction parameterization (Salimans & Ho, 2022) to im- prove training stability, where a and Bg are time-dependent coefficients that balance the contributions of noise and clean data. That is, rather than predicting either the noise or the clean data directly, this parameterization predicts the intermediate variable Vt- The time variable is sam- pled from a noise schedule within the interval t ~ [0, 1]. The scaling functions are defined as a = co.(0.. and or = (.)...tt.).
Naive masking: An inference-time method used in DITTO (Novack et al., 2024b), we implemented it with Stable Audio Open (Evans et al., 2024c) for audio inpainting and outpainting. We initiate the denoising process with random noise x,, and after each denoising step, we immediately overwrite the "reference" region of x with a noisy version of a given reference audio.
We freeze all model components except for the adapters, feature extractors, and the zero-initialized ID convolution layers (as shown in Figure 1). We train two sets of adapters: one conditioned on all musical attributes and the other condi- tioned only on audio, using identical training configurations. We use a batch size of 128, a constant learning rate of 10-1, and a weight decay of 10-2 To encourage the model to focus on Cattr or Caudio- we drop the text condition in 30% of training iterations. Additionally, each condition is inde- pendently dropped with probability of 50% and subjected to random masking. The model is trained for 40,000 steps with an effective batch size of 128 on single NVIDIA RTX 3090. For inference, we fix the separate guidance scales as shown in Table 2. We use 50 denoising steps to generate 47- second audio clips. When both a musical attribute condition and an audio condition are applied, we use a complementary masking strategy. This means that the model is exposed to only one type of condition at a time, as the audio condition can be overly dominant and may cause the model to ignore the musical attribute condition.
4.4. Objective Evaluation Metries
We use the following metrics to evaluate the musical at- tribute controllability, text adherence, and audio realism. In order to benchmark against Stable Audio Open Control- Net (Hou et al., 2025), we use the same open-source code* for calculating FD DDpenn3. KLpasst (Koutini et al., 2021), and CLAP (Wu et al., 2023). FDopent3 (Cramer et al., 2019) extends Fréchet Distance (FD) full-band stereo audio using OpenL3 (48kHz) features, enabling more comprehen- sive similarity evaluation. KI Luss measures semantic align- ment via KL divergence using PaSST, an audioSet-trained tagger (Gemmeke et al., 2017), adapting it for long-form audio by segmenting and averaging logits. CLAP score (Wu et al., 2023) assesses text-audio correspondence using CLAP embeddings with a feature fusion approach, ensuring robust evaluation of long-form, high-resolution audio.
4.3. Baselines
Melody Accuracy We directly use the code for caleu- lating melody accuracy from Stable Audio Open Control- Net (Hou et al., 2025), provided by the authors, to ensure a fair comparison. The chromagram C' & R12xT is computed via STFT with a window size of 2048 and a hop size of 512, and we use a argmax to select the most prominent pitch. We measure the agreement between the frame-wise pitch classes (C, C#,, .... B; 12 in total) of the reference audio and the generated audio. Higher accuracy indicates better preservation of the intended melody.
Although musical attribute control, audio inpainting, and outpainting have been explored in many prior works, they are either not open-source (e.g.. Music ControlNet (Wu et al., 2024), DITTO (Novack et al., 2024b;a), JASCO (Li et al., 2024)) or generate a relatively short audio (Tal al., 2024). MusiConGen (Lan et al., 2024) and Coco-Mulla (Lin
et al., 2023) provide rhythm control; however, MusiConGen represents rhythm using constant beats-per-minute (BPM) value, while Colo-Mulla relies on a separate drum track for rhythm representation. We consider both approaches unsuitable for direct comparison with our method.
"https: / //ggggttuhb.h com/ /tm//ttabllityAA/ s stable audiiaudi o metrics
6

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
Table Melody control comparing with state-of-the-ar controllable text-to-music generation models. The proposed model achieves the best melody accuracy and acceptable musical quality metrics with fewer trainable parameters and training data.
Trainable Parameters
Model
Total Parameters
Training Data
FD +
KL
CLAP 1
Mel acc. 1
SasiiCeen-Steree- Berro-LLage-Mellln
3.3B
3.3B
20K hr
193.66
0.436
0.354
43.1
Stable Audio Open ControlNct
572M
1.98
2.2K hr
97.73
0.265
0.396
56.6%
Ours (MuseControll ite-Melody)
85M
1.4B
1.7K hr
76.42
0.289
0.372
61.1 %
Ours (MuseControll Aite-Attr)
85M
1.4B
1.7K hr
80.79
0.271
0.373
60.6%
Table Performance of all combinations of controls using text and Cau from different subsets of Song Deseriber Dataset (Manco al., 2023) (style transfer task). Controllability significantly improves when the relevant condition is provided to the model (V
melody
rhythm dynamics
FD
KL
CLAP
Mel acc.
Rhy F1
Dyn
Text condition only
X 124.77
0.59
0.42
0.09
0.21
0.05
/
X
89.89
0.54
0.28
0.60
0.76
0.66
single musical attribute
X
/
X
111.03
0.45
0.38
0.09
0.89
0.42
X
x
136.40
0.54
0.39
0.09
0.30
0.92
X
90.00
0.56
0.28
0.61
0.89
0.73
text---double musical attributes
V
92.73
0.55
0.28
0.60
0.78
0.94
X
V
119.20
0.46
0.35
0.08
0.89
0.93
Text++all musical attributes
V
94.50
0.55
0.28
0.61
0.90
0.95
Table Performance of all combinations of controls using text and cau from the same subset Song Describer Dataset (Manco al.. 2023) (non-style transfer task).
melody
rhythm
dynamies
FD
KL.
CLAP1
Mel acc. f
Rhy FI
Dyn cor.
Text condition only
X
124.77
0.58
0.42
0.09
0.22
0.06
/
X
83.89
0.31
0.37
0.61
0.77
0.67
single musical attribute
X
/
X
104.83
0.41
0.40
0.09
0.86
0.48
X
x
133.43
0.52
0.40
0.09
0.32
0.92
X
86.62
0.30
0.39
0.61
0.89
0.74
Text-i-ouble musical attributes
V
88.30
0.28
0.38
0.60
0.78
0.94
X
/
110.60
0.36
0.40
0.09
0.88
0.93
Text+tall musical attributes
V
90.28
0.31
0.39
0.61
0.89
0.95
Dynamics Correlation Following Wu al. (2024), we
To evaluate the smoothness of bound- aries in audio inpainting and outpainting tasks, adopt the Novelty- -ased Segmentation approach (Müller, 2015). First, compute linear spectrogram and construct the self-similarity matrix (SSM) by measuring pairwise similar- ities between feature vectors at different time frames. The SSM is then convolved with a checkerboard-sheee -sapped kernel centered at each diagonal position. This kernel is designed to emphasize local changes by contrasting regions of high and low similarity. We select kernel size of 3 detect novelty on short time scale, with other hyperparameters same as the tutorial of Müller (2015). Finally, the convolu- tion values are summed for each time position, forming a
Smoothness Value
compute Pearson's correlation measure the strength of the linear relationship between the dynamics curve of the generated audio and the ground truth condition.
Following Wu et al. (2024), the Rhythm F1 score is computed following standard beat and downbeat de- tection evaluation methods (Davies et al., 2009; Raffel et al., 2014; Wu et al., 2024). It measures the alignment between beat and downbeat timestamps estimated from the input rhythm control and those extracted from the generated au- dio. These timestamps obtained using Hidden Markov Model (HMM) post-filter (Krebs et al., 2015) applied to frame-wiss beat and downbeat probabilities. In accordance with (Raffel et al., 2014; Wu et al., 2024), an input and generated (down)beat timestamp are considered aligned if their temporal difference is less than 70 milliseconds.
Rhythm F1
httpps:/ww..odioolaas-eeere de/resoources/MIR/MM CTTTRR//MP//////S/// /C4/C484_ .hmml

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
one-dimensional curve known as the novelty curve V. This curve represents abrupt changes in musical content over time, with peaks typically indicating segment boundaries. We define the Smoothness Value as the second finite differ- ence of the position Vú, which is V 2V, + Vi+1. where i represents an inpainting or outpainting boundary position.
fer task for FD, KL, and CLAP. The degradation of CLAP and KL. in style transfer when using melody controls may indicate that the melody condition itself includes the tim- bre or genre information of the original audio. Hence, the generated audio still retains a certain level of information relevant to the original audio. When rhythm and/or dynam- ics controls are applied, the degradation of CLAP and KL is milder. On the other hand, musical attribute controllability remains approximately the same for both the style transfer and non-transfer tasks.
A lower Smoothness Value implies non-smooth transition.
5. Result
5.1. Melody conditioned generation Comparison
5.3. Audio Outpainting and Inpainting
To compare melody--conditioned generation with Stable Au- dio Open ControlNet (Hou et al., 2025) and MusicGen- Melody (Copet et al., 2024), we train another version of MuseControlLite that learns only from melody conditions. This version, referred to as MuseControll CeCCoootol.iteMMellie MeloMeo is trained for 40,000 steps with a batch size of 128. Addition- ally, we report results for adapters trained with all musical attributes but used for melody-only conditioned generation, referred to as MuseeontrolLite-AAtrr The results are pre- sented in Table 3. Despite having fewer trainable parame- ters and less training data, both MuseControlLite- Mieteyyeeyyyy and MuseControll Atte outperform other baselines in terms of FD and melody accuracy, demonstrating superior controllability and realism. However, our models perform slightly worse than Stable Audio Open ControlNet (Hou et al., 2025) on KL and CLAP metrics. This may be due to our training data being sourced solely from the MTG- Jamendo dataset (Bogdanov et al., 2019), whereas Stable Audio Open ControlNet (Hou et al., 2025) uses a more di- verse dataset consisting of MTG-Jamendo (Bogdanov et al., 2019), FMA (Defferrard et al., 2016), MTT (Law et al., 2009), and Wikimute (Weck et al., 2024).
Audio Outpainting We mask 47-second audio clip, re- taining only the first 24 seconds, and experiment with no attribute controls and single musical attribute controls using MuseControlLite (i.c., the one trained to consider both at- tribute controls and audio controls) and other baselines. We trim the first 24 seconds and retain the outpainting parts, as we aim to evaluate only the newly generated segments. As shown in Table 6, our text condition only outpainting out- performs MusicGen-Large in all aspects except for Rhythm F1, demonstrating superior audio realism and consistency. Notably, our non-autoregressive model achieves better ob- jective results than the state-of-the-art autoregressive model, despite autoregression being intuitively preferred for con- tinuation tasks. This suggests that our model effectively learns improvise missing segments using cross-attention layers, even when no audio condition is provided. While the naive masking method achieves better text adherence according to the CLAP score, it has the lowest smoothness value, indicating abrupt changes at the boundaries.
Audio Inpainting To evaluate audio inpainting, we retain only the first and last seconds of the reference audio, test- ing the model's ability to fill in the missing middle portion, Similar to the outpainting task, we exclude the reference segments from the evaluation and assess only the generated inpainted part (i.c., the generated audio from 16s to 32s). The results, presented in Table 7, show that in terms of audio realism and text adherence, the performance is similar to that of audio outpainting. However, musical attribute control appears to be more challenging. A possible reason is that, in audio inpainting, the model must handle two transitions one at the beginning and one at the end-rather than simply continuing the sequence, making the task inherently more complicated.
5.2. Ablation Study for Musical Attribute Conditions
We evaluate all combinations of controls on both style transfer and non-style transfer tasks using MuseControlLite- Attr, with the results presented in Tables 4 and 5. While MuseControlLite-ANe is trained on all musical attribute conditions, it allows users to perform inference with any combination of these conditions. The style transfer task uses musical attributes and text from different subsets, while the non-style transfer task uses musical attributes and text from the same subset. The results in Table 4 are consistent with what reported in Music ControlNet (Wu et al., 2024). When the condition is provided, the melody accuracy, rhythm FI, and dynamics correlation are significantly higher. Addi- tionally, we found that when only the melody condition is given, the generated audio still achieves high rhythm F1 and dynamics correlation, suggesting that the melody con- dition provides rich information that may include rhythm and dynamics cues. Comparing Tables 4 and 5, we find that MuseControlLite-ANe performs worse on the style trans-
5.4. Subjective Evaluation
The task for subjective evaluation is similar to Section 5.1, but the dataset consists of samples from the demo website of Stable Audio ControlNet (Hou et al., 2025). We apply the
8

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
Table 6. Result for audio outpainting task.
Smoothness (at 24s)
Model
FD
KL
CLAP t
Mel acc.
Rhy F1
Dyn cor
MusicGen-Stereo- Ltrrettta---a--MMlllall
207.48
0.38
0.32
0.19
0.71
0.13
-0.18
Bascline
Naïve masking
193.92
0.64
0.39
-0.07
0.23
0.11
-0.40
Audio
184.55
0.37
0.34
0.14
0,45
0.14
-0.16
Mudio
138.07
0.27
0.35
0.57
0.81
0.43
-0.26
Ours
Text--Rudon
165.55
0.32
0.35
0.15
0.95
0.26
-0.16
Text+Auuio-DDnammicc
189.15
0.32
0.35
0.14
0.55
0.77
0.10
Table 7. Results for audio inpainting
Smoothness (at 16s)
Smoothness
KL
CLAP
Mel acc.
Rhy F1 f
Dyn cor.
(at 32s)
Baseline
Naïve masking
193.92
0.64
0.39
0.12
0.29
0.07
-0.62
-0.53
Audio
144.23
0.26
0.30
0.17
0.51
0.14
-0.36
-0.30
Text+Audio- Audio-
127.00
0.33
0.31
0.57
0,80
0.41
00.56
-0.13
Ours
Text A
144.96
0.26
0.31
0.17
0.89
0.21
-0.08
-0.77
Audio-+- Dynamics
150.90
0.29
0.29
0.17
0.61
0.74
-0.40
-0.59
6. Limitations
Table 8. User study mean opinion scores (1-5) comparing text adherence (T), melody similarity (M), and overall preference (O),
Although MuseControlL has demonstrated superior per- formance in controllable music generation, there are still few weaknesses: (i) Using multiple classifier-free guid- ance mechanisms for flexibility slightly slows inference due to multi-batch processing.
Model
T
M
O
MusicGen-Stereee Laao--arge--eleee Stable Audio Open ControlNct
3.12
2.67
3.06
3.69
4.17
3.65
Ours (MuseControlLite)
3.58
4.21
3.63
(ii) For audio inpainting and outpainting, if the prompt deviates significantly from the reference audio, MuseControll.in struggles gen- erate smooth transitions. (iii) MuseControlLite is trained solely on the public MTG-Jamendo dataset (Bogdanov et al., 2019), which contains mostly electronic music. Therefore, MuseControlLite would perform better on other genres after being fine-tuned with a more diverse dataset.
same text and melody conditions demonstrated their site to generate music using both our model and MusicGen. These outputs are then compared to the samples retrieved from their demo page. Participants rate each audio on a 5-point Likert scale based on the following three aspects:
7. Conclusion
Text adherence (T): Does the generated audio match the text prompt?
In this paper, we have introduced MuseControlLite. lightweight training method that not only enables precise control over music generation under specified musical at- tribute conditions, but also supports audio outpainting and inpainting, either through text-only condition generation or with musical attribute control. We benchmark our approach against saate-of-the-eee aar ControlNet-basse methods (Zhang et al., 2023; Hou et al., 2025) for structural control. MuseC- ontroll Lite demonstrates superior results, suggesting that it is powerful alternative to competing models.
Melody similarity (M): Does the generated audio faith- fully preserve the melody from the reference audio?
Overall preference (O): Overall, how much do you like the generated audio?
The Stable Audio ControlNet (Hou et al.. 2025) demo page provides eight music editing samples. We divided these into four questionnaires, each containing two melody- conditioned audio samples and three audio samples gener- ated by our baseline methods. total of 34 participants recruited from social circle and randomly assigned to one of the four questionnaires. According to subjective results, MuseControllite demonstrates performance compa- rable to that of Stable Audio Open ControlNet (Hou et al., 2025) while using fewer training parameters and fewer train-
Promising future directions include: (i) manipulating the at- tention mechanism for more efficient training and improved control precision, and (ii) enhancing control over conditions that cannot be accurately extracted using current feature extraction methods.
ing data.

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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
Impact Statement
Learning follow image editing instructions. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18392-18402, 2023.
MuseControlLite lowers the barrier for precise, time- varying control in text-to-music generation, making ad- vanced creative tools more accessible to a broader range of artists, hobbyists, and researchers. By introducing lightweight fine-tuning mechanism with fewer trainable pa- rameters, we enable resource-comee constrained developers to in- legrate powerful controllability features into their systems without requiring large-scale computational infrastructures. This democratization of sophisticated AI-driven music cre- ation can foster new waves of artistic experimentation, sup- port rapid prototyping for commercial applications, and reduce the technical overhead that often limits innovation.
Chu, Y., Xu, J., Yang, Q. Wei, H., Wei, X., Guo, Z., Leng, Y., Lv, Y., He, J., Lin, J., et al. Qwen2-audio technical report. arXiv preprint arXiv2207.107554 2024.
Clark, K. Electra: Pre-training text encoders as dis- criminators rather than generators. arXiv preprint arXiv:2003. 10555, 2020.
Copet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G., Adi, Y., and Défossez, A. Simple and controllable music generation. Advances in Neural Information Pro- cessing Systems, 36, 2024.
At the same time, these capabilities raise important ques- tions about intellectual property, cultural expression, and the changing role of human creators. While MuseControl- Lite offers transformative udvantages-Suut as facilitating customized soundtracks, educational tools for music learn- ing, and expanded accessibility for individuals with limited musical training-it also highlights the need for responsible use. We encourage practitioners to adopt transparent data governance and to respect copyright laws and cultural con- texts when generating music. By balancing creative freedom with ethical considerations, MuseControlLite has the poten- tial to advance the state of music AI while emphasizing responsible and respectful innovation.
Cramer, A. L.. Wu, H.-H., Salamon, J., and Bello, P. Look, listen, and learn more: Design choices for deep audio embeddings. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3852-3856. IEEE, 2019.
Davies, M. E., Degara, N., and Plumbley, M. D. Evalua- tion methods for musical audio beat tracking algorithms. Queen Mary University of London, Centre for Digital Music, Tech. Rep. C4DM-7R-09-06 2009.
Defferrard, M., Benzi, K., Vandergheynst, P., and Bresson, X. Fma: A dataset for music analysis. arXiv preprint arXiv: wr1vv1662.0018000 2016.
Acknowledgment
This work is supported by grants from Google Asia Pacific and the National Science and Technology Council of Taiwan (NSTC 12-2222--E022-0055
Elizalde, B., Deshmukh, S., Al Ismail, M., and Wang, H. Clap learning audio concepts from natural language su- pervision. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5. IEEE, 2023.
NSTC 1 114-2224M--00 -0002- 003, NSTC 1 113-2628-E-002-000 NTU-1 1904-5). The authors sincerely appreciate the valuable feedback provided by the anonymous reviewers. We also thank Prof. Chris Donahue from Carnegie Mellon University for providing insights for this work. Additionally, we are grateful to the authors of Hou et al. (2025) for helping us align our training and evaluation settings.
Evans, 7.,, Carr, C.,, Taylor, J., Hawley, S. H., and Pons, latent audio diffusion. arXiv
J. Fast preprint arXiv:2402.04825 2024a.
Evans, Z., Parker, J. D., Carr, C., Zukowski. Z., Taylor, J., and Pons, J. Long-form music generation with latent diffusion. arXiv preprint arXiv:2404. 0..4004.0301, 10301, 2024b.
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MuseControlLite Multifunctional Music Generation with Lightweight Conditioners
A. Separated guidance scale formulation
To expand classifier-free guidance from a single condition to a general form, we start from:
plz, .l.......... p(x) II p(c/ a.....,-11..
(11)
We simply apply the Bayes" rule:
7
p(z) II p(al 9......-11
plale,,.... cx)
(12)
plc,...., Cn)
Use log scale convert multiplications additions:
p(gle,,......... Gx) = log(x)) +
log Dgg(( cr, ..-... - loggle, C.).
(13)
Take the derivative to eliminate the constant term:
a logp(o/cc...... = Va logp(s) + Σ V logp(et/ Gn...,.-i,,
(14)
n
a
Σ va log (cal(, cI,..., ci-1) Σ
v. n log plogp((c,,,, cI....,-11) logp(ı, en G-I)..
(15)
Then scale the condition term with guidance A The guidance A be different according to the control strength that is required.
v log G.) = v log Σ V. (ogg((css (- logp(æ, c
(16)
leading to Equation (9) shown in Section 3.5.
B. Ablation study for all key module in MuseControlLite
We conducted an ablation study on the key modules of our proposed method. Without rotary positional embeddings (RoPE)
(Su et al., 2024), the model cannot perform melody control, although still achieves the highest CLAP (Hlizalde et al., 2023)
score. Removing the ID-CNN condition extractor causes all metrics to drop, and omitting the zero-initialized ID-CNN layers used to sum cross aoos-attentionn outputs similarly degrades performance. Finally, doubling the number of attention heads by scaling W and We yields no improvement compared to the base configuration.
Table 9. Performance of all combinations of controls using conditions extracted from Song Describer Dataset (Manco al., 2023).
Extractor
ROPE
Scale up
CNN
FD |
KL +
CLAP 1
Mel acc.
Ours
78.50
0.29
0.38
58.6%
/
Ours w/o ROPE
X
X
113.13
0.57
0.41
10.7%
V
Ours w/o Extractor
X
x
94.50
0.33
0.38
57.2%
Ours w/o CNN
X
93.30
0.30
0.37
56.7%
Ours w/ double heads
/
80.25
0.29
0.38
58.4%
13

---

MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
Untrained Model
Trained Full Audio Condition
Trained Outpainting (10th onward)
Trained Inpainting (5th-25th sec)
Figure This figure consists of four attention maps: i) untrained, ii) trained, given full audio condition, iii) trained, inpainting 5th 5th-25th seconds, and iv) trained, outpainting 10th seconds onward. After training, portion of the atlention maps exhibits perfectly diagonal pattern when given the full audio condition. When performing inpainting or outpainting (i.e. only partial audio condition given), the model tends reference previous keys, exhibiting effective usage of given audio context.
14


Reviews:
[{"review_id": "b07kGzp2Wn", "reviewer": "Reviewer_c3ce", "summary": "The paper introduces MuseControlLite, a parameter-efficient methodology for aligning a pre-trained, DiT-based text-to-music model, to both symbolic and audio controls. The authors demonstrate the capability of MuseControlLite to extend the controllability of a pre-trained StableAudio-Open model, from text prompts, to conditioning on melodies, dynamics, rhythm and audio excerpts for inpainting and outpainting, with light-weight zero-convolution additive adapters operating on decoupled cross-attention layers.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the insightful and valuable feedback. In response to your comments, we have made several plans to update to our submission, as outlined below.\n\n#### On Claims & Evidence:\n*Firstly*, you are right that JASCO has effectively integrated symbolic and audio controls. We will revise the paper to reflect this accurately. For example, in the second contribution outlined at the end of Section 1, we will update the phrasing from \"first trainable model\" to \"first trainable lightweight adapter\".\n\nFor the reviewer's information: Jasco differs from ours in that it's trained from scratch and that it generates 10s audio. Moreover, Jasco's audio condition is quantized to facilitate style transfer, while we use full-resolution audio for in/out-painting.\n\n*Secondly*, we initially omitted a subjective evaluation due to the unavailability of the code and weights for the key baseline, Stable-Audio ControlNet. However, in response to multiple reviewers’ requests, we have conducted a **listening test** by incorporating examples from the Stable-Audio ControlNet project website, despite the possibility that these samples may have been cherrypicked. We recruited 34 participants and used the same text and melody conditions as those demonstrated on their website. We generated music using both our model and MusicGen-Melody, then compared these outputs with the samples retrieved from their demo page. Due to space limit, we present the detailed results in our response to Reviewer 5zZP as Table C. The results show that, when equipped with the same melody representation as Stable-Audio ControlNet, our model performs favorably, despite requiring significantly fewer trainable parameters. We appreciate the reviewer’s suggestion, which prompted this valuable addition.\n\nWe wish to highlight that the perceived inferiority of our model’s output on our original demo site stemmed from our use of a different melody representation than that of Stable-Audio ControlNet. This oversight was identified thanks to a comment from Reviewer 5zZP. Specifically, our initial model (denoted as `v1`) adopted the melody representation from MusicGen-Melody, which offers lower pitch resolution compared to that employed by Stable-Audio ControlNet. For fair comparison, we have retrained our model and developed a new version (`v2`), which now aligns with Stable-Audio ControlNet’s melody representation. This change significantly enhances the perceptual quality of our generated samples. We have updated our demo website accordingly, and we invite the reviewer to explore the improved results.\n\n*Finally*, we share the reviewer’s intrigue regarding potential information leakage and have addressed this concern by implementing the suggested **style transfer** experiment. Using the Song Describer Dataset, we divided it into two disjoint subsets. We generated samples by pairing text from the first subset with attributes extracted from the second subset, ensuring that musical attributes are independent of the ground-truth audio. The generated samples were then evaluated against the first subset as the reference set. As presented in Table A below, the results align with our prior findings: using more conditions enhances FD and KL scores. We will replace the Table 4 of our paper by this new Table A.\n\nBesides, since the text and melody conditions in the aforementioned user study were from distinct music clips, we have also extended this \"style transfer\" approach to a human evaluation.\n \n#### Table A\n\n|Grp|Melody|Rhythm|Dynamics|FD|KL|CLAP|Mel Acc.|Rhythm F1|Dyn cor.|\n|---|---|---|---|---|---|---|---|---|---|\n|None|–|–|–|185.48|0.67|0.36|0.10|0.22|0.08|\n|Single|✓|–|–|139.89|0.49|0.37|0.69|0.43|0.17|\n|Single|–|✓|–|158.64|0.62|0.34|0.10|0.85|0.47|\n|Single|–|–|✓|189.05|0.64|0.34|0.10|0.49|0.93|\n|Double|✓|✓|–|**124.38**|0.49|0.34|**0.70**|0.87|0.52|\n|Double|✓|–|✓|145.00|**0.42**|**0.38**|0.69|0.67|0.94|\n|Double|–|✓|✓|173.93|0.56|0.32|0.10|**0.88**|**0.95**|\n|All|✓|✓|✓|138.18|0.47|0.35|**0.70**|0.86|**0.95**|\n\n#### On Other Weaknesses:\nIn our demo page, we used fixed mask positioning mainly for simplicity. However, during training, we applied random masking ranging from 10% to 90% of the audio condition. This enables arbitrary mask sizes at inference time, ensuring applicability.\n\nThe suboptimal audio quality observed in the inpainted examples likely stems from an overly challenging configuration in our initial setup. Specifically, we masked the middle 20s---2/3 of the total audio duration---while providing only the first 5s and last 5s as context. In contrast, existing models (e.g., DITTO) typically adopt a less demanding setting, such as masking the middle 1/3 of the audio. We acknowledge this oversight and plan to rerun the experiment using this more standard config to see whether it gives improved audio quality.", "claims_and_evidence": "The claims made in the submission are mostly clear and supported by evidence. However, I have the following concerns:\n\n1.The authors present the incorporation of both symbolic and audio controls in music generation as a core contribution of the paper. This claim isn’t aligned with prior work. JASCO, e.g., a prior work cited by the authors, is a trainable text-to-music model that combines text, symbolic and audio controls. \n\n2. The experimental section lacks a subjective evaluation measuring the performance of MuseControlLite in terms of control adherence, and more importantly, in terms of the perceptual quality compared to the baselines.\nListening to samples from the demo page, it is apparent that MuseControlLight is likely inferior in terms of audio quality and musicality compared to the baselines. This might be unaligned with the trend implied by the objective evaluation, in which MuseControlLight is on-par with the baselines in terms of quality and musicality.\n\n3. In Section 5.2. - “Ablation Study for Musical Attribute Conditions” - the authors attribute the reduction in FD, KL and CLAP obtained by adding musical controls as an evidence for improved audio quality and semantic alignment with the reference dataset. While this argument is partially sound, the authors didn’t address the uncertainty of a potential information leakage when conditioning on musical attributes that are a function of the ground truth audio. In specific, the significant reduction in FD, obtained by introducing melody conditioning, might be instead attributed to additional information on the GT samples rather than quality improvement. To make the argument more valid, the authors should experiment with style transferred samples, e.g. the original melody with an out-of-genre text prompt or vice versa, and to validate the trends with a human study.", "methods_and_evaluation_criteria": "In general, the methodology and the evaluation criteria make sense for the problem and for supporting the proposed approach. However, the lack of subjective evaluation significantly reduces my confidence in the effectiveness of the proposed technique.", "theoretical_claims": "I briefly checked the derivation of the multi-source classifier free guidance in appendix A, and I didn’t find any issues.", "experimental_designs_or_analyses": "I checked the design of the following experiments:\n1. Baseline comparison in terms of quality and melody adherence.\n2. Ablation on the influence of the different musical attributes on MuseControlLite performance.\n3. Comparison to baselines in terms of inpainting and outpainting.\n\nI didn’t find major issues beyond what was previously mentioned in the “Claims and Evidence” section.", "relation_to_broader_scientific_literature": "The paper expands prior work on temporally controlled music generation, by introducing an adaptor-based method that is significantly more efficient in-terms of having a light-weight set of trained parameters, compared to prior ControlNet based approaches, such as MusicControlNet. As opposed to MusicControlNet’s control adaptation technique, that requires a set of new weights that is in the order of magnitude of the original model, here, the adaptor consists of a parameter set that is an order of magnitude lighter than the baseline StableAudio-Open model. In terms of the set of musical controls, the paper provides a set of controls that is more diverse than MusicControlNet, but very similar to the control set proposed by DITTO.", "essential_references_not_discussed": "n/a", "other_strengths_and_weaknesses": "Weaknesses: \n* Fixed mask positioning, at least in the demo page, limiting the applicability of the system. \n* Qualitative inpainting results expose a significant degradation of the audio quality in the in-painted areas. The transition between original and in-painted areas sounds unsmooth. \n\nStrengths:\n* The ablation on the effect of ROPE introduction to decoupled cross-attention adapters demonstrates a clear and convincing trend. \n* Planned open-sourcing of code and model checkpoints.", "other_comments_or_suggestions": "* Typo in figure 1, “farword” instead of “forward”.\n* Line 257 - “Since the segments controlled by c_audio are more rigid, we propose to use musical attribute conditions to flexibly control the masked audio segments.” - this is unclear\n* Line 295 - “as we found that latent space length has only a minor influence on audio quality.” - Please add an explanation in addition to the empirical observations.\n* Baselines - section 4.4 - “or not generating a relatively short audio” should be “or generating a relatively short audio”", "questions_for_authors": "n/a", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yv8GZSHS6x", "reviewer": "Reviewer_YDrx", "summary": "The paper introduces MuseControlLite, a lightweight fine-tuning mechanism for text-to-music generation that extends previous control work. \n\nIts main contributions include a new adapter design using decoupled cross-attention with positional embeddings for time-varying musical attributes. \n\nThe model claims to control melody, rhythm, and dynamics—and supports both inpainting and outpainting tasks—with significantly fewer trainable parameters compared to some existing methods.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your supportive feedback and hope that our responses below address and alleviate your major concerns.\n\n#### On the Need of More Empirical Evidence:\n\nAs elaborated in our response to Reviewer c3ce, we conducted a user study to bolster the empirical rigor of our paper. Initially, we excluded a subjective evaluation due to the unavailability of the code and weights for the key baseline, Stable-Audio ControlNet. However, in response to multiple reviewers’ requests, we have conducted a listening test by incorporating examples from the Stable-Audio ControlNet project website (https://stable-audio-control.github.io/web/), despite the possibility that these samples may have been selectively chosen by the authors. For this evaluation, we recruited 34 participants and utilized the same text and melody conditions as those demonstrated on their website. We generated music using both our model and MusicGen-Melody, then compared these outputs with the samples retrieved from their demo page. We put the detailed results of this listening test in our response to Reviewer 5zZP as Table C. The results demonstrate that, when equipped with the same melody representation as Stable-Audio ControlNet, our model performs favorably, despite requiring significantly fewer trainable parameters. We believe this strengthens the robustness of our findings. \n\n#### On Missing Comparison with Coco-Mulla:\n\nCoco Mulla employs parameter-efficient fine-tuning (PEFT) to enhance the pretrained MusicGen model, enabling control over chord, rhythm, and piano roll features. It would have served as a great baseline for our study, facilitating comparisons with both larger adapters (e.g., ControlNet) and models with varying numbers of trainable parameters (e.g., Coco Mulla). However, a direct comparison is confounded by differences in rhythm representation between our approach and Coco Mulla’s, as well as by the distinct pretrained backbone models utilized. Additionally, Coco Mulla’s prefix-based conditioning strategy is optimized for language models (LMs) rather than diffusion models, rendering it incompatible with the diffusion-based architecture of Stable Audio Open. Consequently, we have excluded this empirical comparison from our paper.\n\nNevertheless, we concur with the reviewer’s observation that Coco Mulla’s parameter-efficient fine-tuning approach for text-to-music generation merits recognition. We will revise the final version of the paper to acknowledge this contribution appropriately.\n\n\n#### On Missing Discussion on Limitations:\n\nWe concur that such discussions are essential for evaluating potential trade-offs and identifying limitations in our approach. Accordingly, we intend to incorporate the following discussions on the weaknesses of our model into the final version of the paper\n- The fine-tuning approach, which employs decoupled cross-attention along with rotary positional embedding and zero convolution, becomes unnecessary when training from scratch is feasible.\n- The generated distribution of our model is largely influenced by the training dataset used for the pretrained backbone.\n- Using multiple classifier-free guidance requires passing multiple batches during inference, which slightly reduces inference speed.", "claims_and_evidence": "I personally like this work, which showing great results, but I need to point out that the claim needs more evidence and a wider and fair comparison.\n\nWhile the paper presents experimental results that show improvements in control accuracy—particularly in melody control—the evidence for some claims is not entirely convincing. \n\nFor instance, the parameter efficiency claim is undermined by an unfair comparison: one baseline (coco-mulla) reportedly uses only 4% of parameters (about 60M), yet is dismissed by the authors as unsuitable for comparison. \n\nThis raises questions about whether the reported gains in control precision are solely attributable to the proposed design.", "methods_and_evaluation_criteria": "The proposed method of integrating positional embeddings into decoupled cross-attention layers appears reasonable for managing time-varying conditions in music generation. \n\nThe evaluation criteria (including melody accuracy, rhythm F1 score, and audio realism metrics) are standard and appropriate for this domain. \n\nHowever, the method largely extends existing approaches rather than introducing fundamentally new ideas, which somehow limits its novelty.", "theoretical_claims": "The paper does not provide deep theoretical proofs or rigorous analyses to substantiate its claims. \n\nWhile the discussion on the importance of positional embeddings is interesting, the theoretical foundation remains somewhat informal. \n\nNo detailed proof is provided for the improvements claimed, so the correctness of any theoretical claims is not thoroughly validated.", "experimental_designs_or_analyses": "The experiments are comprehensive, addressing multiple control aspects (melody, rhythm, dynamics) and tasks (inpainting and outpainting). \n\nHowever, the experimental design could benefit from a more balanced comparison against baselines—especially regarding the tuning parameter counts. \n\nAdditionally, the paper does not explicitly discuss its limitations, which makes it difficult to assess the potential trade-offs and areas where the method may fall short.", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "Overall, while the work is interesting and provides a useful baseline—especially if the code is open-sourced—the contribution in terms of theoretical innovation is limited. The paper might be better suited for an applications-focused venue (e.g., ISMIR) rather than a flagship conference like ICML.", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4VLWFTTCdT", "reviewer": "Reviewer_5zZP", "summary": "Within the domain of raw audio music generation, motivated by the need for (1) lighter alternatives for fine-tuning, together with the need for (2) better control accuracy (i.e. for the user), the authors propose MuseControlLite, a system for time-varying condition control for music generation. MuseControlLite reduces parameter count relative to, e.g. Music ControlNet (Copet et al 2024), by using decoupled cross-attention layers (Ye et al 2023) in a diffusion transformer, and then to get this to work well they incorporate a modification of rotary positional embedding (Su et al 2024). \n\nThe resulting model supports joint attribute and audio control, and they train a separate set of adapters for audio conditioning to allow inpainting and outpainting. Results are presented on the public Song Describer benchmark (Manco et al 2023) and they report a 14% improvement in melody accuracy. Example generated audio files are provided on a demonstration website.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the insightful and valuable feedback, which has inspired us to make several significant updates into our work, as detailed below. We hope the reviewer will agree that these revisions greatly enhance the scientific quality of the paper.\n\n#### On Melody Representation:\n\nThanks to your valuable comment, we have identified an oversight that causes the perceived inferiority of our model’s output on the original demo website. Specifically, our initial model (denoted as `v1`) adopted the melody representation from MusicGen-Melody, which offers lower pitch resolution compared to that employed by Stable-Audio ControlNet. To ensure a fair comparison with Stable-Audio ControlNet, we have retrained our model and developed a new version (`v2`), which now aligns with Stable-Audio ControlNet’s melody representation. This adjustment significantly enhances the perceptual quality of our generated samples. \n- v1: This version employs a one-hot 12-pitch-class chromagram as melody condition, same as MusicGen-Melody. However, this melody representation lacks octave specificity, causing the model to misinterpret pitch information.  \n- v2: This version adopts a top-4 128-pitch-class CQT to represent melody condition as proposed by Stable-Audio ControlNet. To ensure a fair comparison with Stable-Audio ControlNet, we modified only the conditioning input, leaving the remainder of the pipeline unchanged. \n \nAs shown in Table B below, MuseControlLite v2 outperforms v1 in FD and KL. While v2 exhibits a lower Mel acc. than v1, we recognize that this metric does not fully capture perceptual melody alignment. Thanks to the review comments, we conducted a listening test, detailed below, which confirms that v2 surpasses v1 in melody control performance. \n\n\n#### Table B\n\n|Model|Train Parms|Total Parms|FD|KL|CLAP|Mel Acc.|\n|-----|-----------|-----------|--|--|----|--------|\n|MusicGen-stereo-melody-large|3.3B|3.3B|187.0|0.47|0.36|43.7%|\n|Stable-audio ControlNet|572M|1.9B|97.7|0.27|0.40|56.6%|\n|v1|**85M**|**1.4B**|135.5|0.38|**0.40**|**70.9%**|\n|v2|**85M**|**1.4B**|**82.2**|**0.25**|0.38|61.4%|\n\n\n\n\n#### On Missing Listening Test:\n\nInitially, we excluded a subjective evaluation due to the unavailability of the code and weights for Stable-Audio ControlNet. However, in response to multiple reviewers’ requests, we have conducted a listening test by using examples from the Stable-Audio ControlNet project website (https://stable-audio-control.github.io/web/), despite that these samples may have been cherry-picked. For this evaluation, we recruited 34 participants and utilized the same text and melody conditions as those demonstrated on their website. We generated music using both our model and MusicGen-Melody, then compared these outputs with the samples retrieved from their demo page. \n\nAs demonstrated in the following Table C (mean opinion scores ∈ [1, 5]), the results demonstrate that, our v2 model performs favorably with Stable-Audio ControlNet, despite requiring only about 1/6 of the trainable parameters. Moreover, we note that we only used only the MTG-Jamendo dataset for training, while Stable-audio ControlNet used four training datasets (MTG-Jamendo, FMA, MTT, Wikimute). \n\n#### Table C\n\n|Model|Text adherence|Melody similarity|Overall preference|\n|-----|--------------|-----------------|------------------|\n|MusicGen-stereo-melody-large|3.12±0.25|2.67±0.23|3.06±0.23|\n|Stable-audio ControlNet|**3.69**±0.28|4.17±0.23|**3.65**±0.25|\n|Ours v1|3.34±0.27|3.62±0.27|2.93±0.25|\n|Ours v2|3.58±0.20|**4.21**±0.20|3.63±0.22|\n\nWe provide samples in the “Updated Melody-conditioned Comparison” section of the demo page to showcase the audio generated with the new melody condition. These samples are the same ones used in our subjective evaluation, with no cherry-picking at all.\n\n\n#### On Variable Quality of the Examples on the Demo Page:\n\nThe audio samples on our initial demo page were selected at random, resulting in variable quality. Regarding the dynamics and rhythm control samples specifically noted by the reviewer, we wish to clarify that the observed text adherence challenges stem primarily from the characteristics of the training data utilized during both the pre-training and fine-tuning phases.\n\n- Fine-Tuning Dataset: The Jamendo dataset, employed for MuseControlLite, includes limited representation of classical instruments, which constrains the model’s ability to capture such timbres effectively. \n- Pretraining Dataset Text Descriptions: The pretrained Stable Audio model was trained on data with limited musical specificity. Consequently, it struggles to interpret nuanced musical terms such as \"melodic,\" \"legato,\" \"harmonized,\" and other concepts rooted in jazz theory. \n\nWe have updated our demo website in the “Highlighted Audio” section to better showcase the inherent limits of the pretrained model (Stable Audio) in terms of text adherence.", "claims_and_evidence": "Yes, the claims are overall well supported.\n\nOne problematic claim is the somewhat implicit claim that the model handles multiple conditioning signals well (e.g. “These results suggest that the model effectively learns to respond to multiple controls simultaneously, despite the added complexity.” in Section 5.2; and “a lightweight training method that [..] enables precise control of music generation under specified musical attribute conditions” in Section 6, etc). However, listening to the demonstration audio files on the provided website (https://musecontrollite.github.io/web/) indicates that while some certainly do sound good (impressive!), there are quite a few that do not sound good, and/or do not effectively achieve what seems to be the intention. To list a few examples:\n* In “Dynamics Control”, the “recording of a melodic piano solo”  neither sounds like a piano solo (it has other instruments) nor is it melodic; it’s almost purely textural. \n* Also in “Dynamics Control”, the jazz band has the right instrumentation, but is musically incoherent (and I appreciate free jazz, but that is not the issue in this case :)\n* In “Melody Control”, the jazz band version of the Chopin Eb nocturne (Op 9 No 2) does not reflect the melody other than a few seconds here and there; even an experienced musician who knows that Nocturne would likely be unable to guess that that is where the melody is coming from. (See also my comment in the next section on metrics)\n* In “Rhythm Control”, for the Mozart (“Eine Kleine…”) / cello quartet combination, the simple solution would have been to simply return the same basic piece but with a more legato sound, as requested in the text—it is practically already almost in a harmonized string quartet arrangement, whereas the generated example doesn’t sound “harmonized” as requested, and sounds more just like repeated notes (which is fine but not really addressing the text prompt).\n* There are just a few examples; there are others.\n\nI still think that this is an impressive system, and the quality of the audio output, overall, is good! So I believe that the above issue could easily be addressed by (a) distinguishing between quantitative results (which seem to be relatively good) and perceptual quality (which seems to be variable), and (b) adjusting the language/tone in a few places to be more aligned with/reflective of the actual audio outputs, and (c) providing some qualitative discussion about all this, with pointers to some of the examples.", "methods_and_evaluation_criteria": "Relative to the conventions within the music generation community, yes, the evaluation criteria make sense. The metrics include Melody Accuracy, Dynamics Correlation (i.e. the correlation between the dynamics curve of the generated audio with the ground truth), Rhythm F1 (a fairly standard, if somewhat problematic, way to evaluate beat alignment), and self-similarity-matrix-based Novelty Value (Muller 2015). All of these are reasonable choices.\n\nHowever, my comments in the “claims/evidence” section above point to an example (the  Chopin Nocturne (Op9 No2) / Jazz band in “Melody Control”) where the pitch chroma might be somewhat matched (i.e. perhaps decent “melodic accuracy”), but perceptually speaking, the generated melody is effectively unrecognizable. Evaluating generative models is difficult and there are no great solutions at the moment. So, while these metrics are reasonable in context of prior work and available tools, they are very limited. These limitations—both of the system and also of the evaluation metrics—simply need to be acknowledged. \n\n(It is possible that there is something I am fundamentally misunderstanding about the control process, such that my expectations are incorrect; if so I would be glad to be corrected. I think this is slightly unlikely, because there are other examples that do sound good and “as I expected”.)\n\nIn terms of baseline comparisons, there appears to be no other system that accepts the combination of controls that MuseControlLite accepts, and therefore there would be no direct comparison in any case, and some other related systems are infeasible for comparisons for yet other reasons, so in that context, the baselines chosen (i.e. MusicGen, Stable Audio Open ControlNet, and a simple baseline implemented by the authors using Stable Audio Open) also do make sense.\n\nCritically, the authors also provide a fairly extensive set of examples to listen to. (It would be nice if they were marked as “cherry picked” vs “random”, e.g. as done for the ControlNet paper). This is a valuable and important part of a paper on generative methods for audio.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "I read the experimental descriptions and analyses and overall did not notice any issues.\n\nI did notice that perhaps some of the audio examples that “didn’t sound good” were cases where it was just a particularly challenging task, e.g. the Chopin Nocturne / jazz band is not a simple request. Again, it is not clear how to analyze/evaluate/quantify this, but it’s possible that if one were able to explore some of this systematically, then ultimately it might work “in favour” of the proposed system, e.g. the jingle bells-xylophone pairing (in the “Rhythm Control” section) is inherently easier and indeed works reasonably well; perhaps the system does better on the easier ones, which would be entirely fair.", "relation_to_broader_scientific_literature": "I believe the authors do a good job of relating the paper’s contributions to the broader literature, e.g. the need for more/better/local/time-varying control, and the relationship to a variety of other audio music generation models, as well as to a few relevant diffusion models more generally.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "This paper demonstrates an impressive level of engineering combined with some creative innovation and insight in how (and why) to put together many complicated moving parts. In particular, the use of rotary positional embeddings for getting the decoupled cross-attention to work well was very nice.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "If a misunderstanding is leading to my expectations being unreasonable for the quality of audio samples (see my comment above in Section on \"Claims & Evidence'), then I would be glad to try to identify the source of the misunderstanding so that it may be corrected. Otherwise, I would like to see a discussion about the generated audio, as I explained earlier.\n\nThis is an impressive system, and my score is a placeholder; if the above issue can be resolved, I will raise it.\n\n[EDIT APRIL 7 -- Raised Score]", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rY3U6MQ1ig", "reviewer": "Reviewer_Rzqk", "summary": "MuseControlLite is an efficient adapter-based, controllable text-to-music based on Stable Audio Open, by adopting decoupled cross-attention (IP-adapter). The key finding is that for time-varying control signals, integrating suitable positional encoding (i.e. RoPE) to the adapter itself is crucial to achieve good results with relatively small number of parameters for the adapter.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your supportive feedback and hope that our responses below address and alleviate your major concerns.\n\n\n#### On Melody Representation:\n\nThanks to your valuable comment, we have identified an oversight that causes the perceived inferiority of our model’s output on the original demo website. Specifically, our initial model (denoted as `v1`) adopted the melody representation from MusicGen-Melody, which offers lower pitch resolution compared to that employed by Stable-Audio ControlNet. To ensure a fair comparison with Stable-Audio ControlNet, we have retrained our model and developed a new version (`v2`), which now aligns with Stable-Audio ControlNet’s melody representation. This adjustment significantly enhances the perceptual quality of our generated samples. \n- v1: This version employs a one-hot 12-pitch-class chromagram as melody condition, same as MusicGen-Melody. However, this melody representation lacks octave specificity, causing the model to misinterpret pitch information.  \n- v2: This version adopts a top-4 128-pitch-class CQT to represent melody condition as proposed by Stable-Audio ControlNet. To ensure a fair comparison with Stable-Audio ControlNet, we modified only the conditioning input, leaving the remainder of the pipeline unchanged. \n \nAs shown in Table B below, MuseControlLite v2 outperforms v1 in FD and KL. While v2 exhibits a lower Mel acc. than v1, we recognize that this metric does not fully capture perceptual melody alignment. Thanks to the review comments, we conducted a listening test, detailed below, which confirms that v2 surpasses v1 in melody control performance. \n\n\n#### Table B\n\n|Model|Train Parms|Total Parms|FD|KL|CLAP|Mel Acc.|\n|-----|-----------|-----------|--|--|----|--------|\n|MusicGen-stereo-melody-large|3.3B|3.3B|187.0|0.47|0.36|43.7%|\n|Stable-audio ControlNet|572M|1.9B|97.7|0.27|0.40|56.6%|\n|v1|**85M**|**1.4B**|135.5|0.38|**0.40**|**70.9%**|\n|v2|**85M**|**1.4B**|**82.2**|**0.25**|0.38|61.4%|\n\n\n\n\n#### On Missing Listening Test:\n\nInitially, we excluded a subjective evaluation due to the unavailability of the code and weights for Stable-Audio ControlNet. However, in response to multiple reviewers’ requests, we have conducted a listening test by using examples from the Stable-Audio ControlNet project website (https://stable-audio-control.github.io/web/), despite that these samples may have been cherry-picked. For this evaluation, we recruited 34 participants and utilized the same text and melody conditions as those demonstrated on their website. We generated music using both our model and MusicGen-Melody, then compared these outputs with the samples retrieved from their demo page. \n\nAs demonstrated in the following Table C (mean opinion scores ∈ [1, 5]), the results demonstrate that, our v2 model performs favorably with Stable-Audio ControlNet, despite requiring only about 1/6 of the trainable parameters. Moreover, we note that we only used only the MTG-Jamendo dataset for training, while Stable-audio ControlNet used four training datasets (MTG-Jamendo, FMA, MTT, Wikimute). \n\n#### Table C\n\n|Model|Text adherence|Melody similarity|Overall preference|\n|-----|--------------|-----------------|------------------|\n|MusicGen-stereo-melody-large|3.12±0.25|2.67±0.23|3.06±0.23|\n|Stable-audio ControlNet|**3.69**±0.28|4.17±0.23|**3.65**±0.25|\n|Ours v1|3.34±0.27|3.62±0.27|2.93±0.25|\n|Ours v2|3.58±0.20|**4.21**±0.20|3.63±0.22|\n\nWe provide samples in the “Updated Melody-conditioned Comparison” section of the demo page to showcase the audio generated with the new melody condition. These samples are the same ones used in our subjective evaluation, with no cherry-picking at all.\n\n#### On Usability:\n\nWe agree that creating complex melody or rhythm conditions can sometimes be challenging. Our solution is to provide a reference audio sample that contains the desired condition. For example, a user could record themselves humming or clapping, and we can post-process that audio to extract both the melody and rhythm conditions. This approach should also work for dynamics; alternatively, users can simply draw a dynamics curve, which our model will accept.\n\n#### On Questions for Authors:\n\nRegarding which time-varying musical attributes provide the best perceptual control? For *Melody Condition*: Ours (v2) offers the best perceptual control in our opinion, since it provides more comprehensive conditional information. The misalignment between the melody conditions in v1 and v2 is due to the v1 version being limited to only one octave of information. For *Rhythm and Dynamics*, on the other hand, we consider the objective metrics for rhythm and dynamics as well aligned with the human perception.", "claims_and_evidence": "The necessity of positional embeddings for time-varying conditioning signal is intuitive, and this work constitues a timely execution of devising a successful recipe of the efficient control over several musical attributes (dynamic, melody, and rhythm) along with the claimed RoPE application to the IP-adapter.", "methods_and_evaluation_criteria": "The method is a combination of existing techniques (pre-trained Stable Audio Open backbone, IP-adapter, and RoPE) which is technically correct. Evaluation metrics employ commonly found ones (FD, KL, CLAP), along with melody accuracy to measure correctness of the chromagram-based melody condition.", "theoretical_claims": "This paper is mostly empirical, and I find no standout theoretical claims to evaluate. Multi-attribute classifier free guidance (appendix A) has been investigated in existing literature.", "experimental_designs_or_analyses": "The work considered several music generation models with controllability (MusicGen-Melody and a reproduced Stable Audio Open ControlNet). While I acknowledge that this work aims efficient adaptation, the objective metrics do not seem to be a clear win over Stable Audio Open ControlNet. Given that objective metrics often do not correlate with human perception, I believe conducting subjective evaluation over attributes in this work (melody, rhythm, dynamics) through mean opinion scores (MOS) will strengthen the merits of this work.", "relation_to_broader_scientific_literature": "A timely contribution towards controllable music generation, which is a prominent area of research after the success of recent text-conditional music generative models.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "While enabling fine-grained control over musical attributes (dynamic, melody, and rhythm) is a welcomed addition, it also potentially adds complexity to the users depending on how hard it is to obtain and manipulate the attributes to non-experts. I acknowledge that these attributes are also used in previous studies, so the choice of attributes itself is not the drawback of this work.", "other_comments_or_suggestions": "None.", "questions_for_authors": "In the author's opinion, which time-varying musical attributes provide the best perceptual control? The ablation study discussed in Sec 5.2 along with Table 4 and numerical results, but how the differences would translate to human perception of correctness of the control?\n\nI would like to support this important direction (controllable music generation), but the experimental rigor has room for improvement to warrant acceptance. Can the authors consider subjective evaluation, or at very least a qualitative study by adding baseline models, to the demo for the readers to form their opinion? I believe having nuanced findings regarding the relation of controlled attributes and human perception would add significant academic merits especially for this work.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces MuseControlLite, a lightweight methodology to finetune a DiT-based text-to-music model for precise music control.
During the author response period, comprehensive experiments are conducted and the requested revisions are submitted by the author. As a result, most reviewers increase the score ratings, acknowledging that "the authors have indeed made some attempt to incorporate/respond all of my requests", " I think the added experiments along with the subjective evaluation would make this work more convincing". Although there is one reviewer still having concerns on the empirical evidence of the baseline comparison, the authors have tried the best to alleviate the issues. One reviewer even highlights that "I think this paper fits well as a mixed combination of Application-Driven Machine Learning together with Deep Learning (generative models). Overall, it is a adequate paper for ICML. Therefore, I recommend acceptance to this paper.
Decision: Accept (poster)