SyncTrack: Rhythmic Stability and Synchronization in Multi-Track Music Generation
Authors: Hongrui Wang
Fan Zhang
Zhiyuan Yu
Ziya Zhou
Xi Chen
Can Yang
Yang Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Music Generation
Rhythmic Stability and Synchronization
Multi-Track Music Generation
Audio Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Multi-track music generation has garnered significant research interest due to its precise mixing and remixing capabilities. However, existing models often overlook essential attributes such as rhythmic stability and synchronization, leading to a focus on differences between tracks rather than their inherent properties. In this paper, we introduce SyncTrack, a synchronous multi-track waveform music generation model designed to capture the unique characteristics of multi-track music. SyncTrack features a novel architecture that includes track-shared modules to establish a common rhythm across all tracks and track-specific modules to accommodate diverse timbres and pitch ranges. Each track-shared module employs two cross-track attention mechanisms to synchronize rhythmic information, while each track-specific module utilizes learnable instrument priors to better represent timbre and other unique features. Additionally, we enhance the evaluation of multi-track music quality by introducing rhythmic consistency through three novel metrics: Inner-track Rhythmic Stability (IRS), Cross-track Beat Synchronization (CBS), and Cross-track Beat Dispersion (CBD). Experiments demonstrate that SyncTrack significantly improves the multi-track music quality by enhancing rhythmic consistency.

Published as a conference paper at ICLR 2026
SYNCTRACK: RHYTHMIC STABILITY AND SYNCHRO- NIZATION IN BULTI-TRACK MUSIC GENERATION
Hongrui Wang",, Fan Zhang ",, Zhiyuan Yu', Ziya Zhou', Xi Chen', Can Yang Yang Wang"
DDepartment of Mathematics, The Hong Kong University Science and Technology
"State Key Lab of CAD&CG, Zhejiang University
3 "Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology
Stttt Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology 'The University of Hong Kong
fhwangfb, z2houcp, xchengx (eneengxxxcconeett .utt.kk.... yang. wang@hku. {macyang, ma mafzhang)@ust hk, zhiyuan. yawaannyuuuueeee. edu. cn https:/synntrrakk-l..gg Lighhhh
ABSTRACT
Multi-track music generation has garnered significant research interest due its precise mixing and remixing capabilities. However, existing models often overlook essential attributes such rhythmic stability and synchronization, leading a focus on differences between tracks rather than their inherent properties. In this paper, we introduce SyncTrack,, synchronous multi- celli-trrcc waveform music generation model designed to capture the unique characteristics of multi-track music. SyncTrrack features novel architecture that includes track-shared modules to establish common rhythm across all tracks and track-specific modules accommodate diverse timbres and pitch ranges, Each track-shared module employs tross-track attention mechanisms synchronize rhythmic information, while each track-specific module utilizes learnable instrument priors to better represent timbre and other unique features. Additionally, enhance the evaluation of multi-track music quality by introducing rhythmic consistency through three novel metrics: Inner-track Rhythmic Stability (IRS), Cross-track Beat Synchronization (CBS), and Cross-track Beat Dispersion (CBD). Experiments demonstrate that SyncTrack significantly improves the multi-track music quality by enhancing rhythmic consistency.
INTRODUCTION
Advances in music generation from raw audio have enabled systems to produce music conditioned on user specifications such genre, mood, tempo, and instrumentation. The mixed waveforms produced by current music generation methods often lack the flexibility needed for professional editing, limiting their usefulness for musicians (Cano et al., 2018). In music production, it's important to manipulate individual instrument tracks, including mixing, rearranging or adding new instruments. Recently, many works (Mariani et al. 2023 Karchkhadze al. 2025 Parker al. 2024 Yao et al.) 2025b) focus on multi-track audio generation. Unfortunately, beat stability and synchronization not considered in generation of multi-track music and its quality evaluation. As the primary organizing element of time in music, rhythm provides the essential framework upon which melody and harmony are built (Abrassart & Doras 2022). Temporal prediction errors caused by irregular beats or inter-track desynchronization lead to persistent violations of auditory expectations, inducing listener discomfort [Mas-Herreed et al.) 2018).
To date, existing methods are incapable of generating satisfactory music with coherent and stable rhythm due to the mismatch between their model architecture and the inherent properties of multi-track
"quual contribution.
Corresponding author.

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O00
O
OOO
000
000
Track-shared module
Track-spocific module
Unified model
Track-shared module
O0O
000
O
000
p0O Guiter
o00
O0o
Bao
Guitar
Drum
Piano
Bass
Dram
Piano
MSDM, MSG-LD, etc.
(b) Sync'Track
Figure 1: (a) Previous methods (Mariani et al. 2023; Karchkhadze et al., 2025) leverage a unified model to learn the joint distribution of multi-track audio stems. (b) While our proposed SyncTrackk incorporates both track-shared modules and track track-specific modules for common and specific informa- tion between tracks.
music. As shown in Fig. [a), MSDM (Mariani et al. 2023) and MSG-LD (Karchkhadze et al. 2025 aim to learn the joint distribution of multi-track audio stems. They treat this task as multivariable time series (MTS) or video generation, which contains complex interactions and huge discrepancy between variables or channels (Wang et al.] 2022 Qiu et al., (025). In contrast to MTS and video generation, multi-track music generation faces unique challenges. In the multi-track music generation task, we need to simultaneously generate music for multiple tracks, with each track representing independent layers or channels in a musical piece, for example, bass, drum, and so on. Two core aspects govern this process: 1) Rhythm refers to the structured temporal arrangement of musical events. Within a single track, rhythm must exhibit stability, meaning that note onsets and beats adhere consistently to regular metrical grid. Across multi tracks, rhythm requires synchronization, ensuring that events from different instruments align precisely with each other and a shared underlying pulse. 2) Timbre defines the unique perceptual quality of sound that distinguishes one instrument from another, such as the warm character of a bass or the bright attack of a piano. These two types of information are different: 1) good musical rhythms possess synchrony and are more harmonious, so rhythm information is shared across tracks; 2) timbre, on the other hand, is the unique information retained by each track, with tracks being mutually independent. MSDM and MSG-LD overemphasize on the inter-track discrepancies, overlooking the common rhythm information. Consequently, neither model achieves satisfactory results in either subjective audio quality or rhythmic consistency.
To solve the above challenges, we propose synchronous multi-track waveform music generation model, named SyncTrrack, with a novel architecture suitable for capturing data characteristics. Specifically, to handle common and specific information of multiple tracks, SyncTrack adopts unified architecture that incorporates both track-shared modules and track-specific modules, as shown in Fig. bb). In the track-shared modules, devise two types of cross- track attention submodules to further capture cross-trasd track rhythmic stability and synchronization: 1) global cross-trace attention is essential for modeling global stability, ensuring that all tracks maintain consistent rhythm framework over the entire piece; 2) time-specific cross-track attention is critical for fine-grained synchronization, aligning musical events across tracks at the same temporal position. In the seec-spcccfic modules, we construct the learnable instrument prior for each track. We incorporate the prior into the latent representation of each track in the track-specific modules. By doing so, timbre and other track-specific features can better capture the differences between tracks. Furthermore, the separation between track- shared and track-specific modules structurally simple and could be dropped into other latent- diffusion systems.
Furthermore, we innovatively propose introducing rhythmic consistency in the evaluation of multi- track music quality. Current works assess the quality of multi-track audio generation merely using the Fréchet Audio Distance (FAD) (Kilgour et al. 2018). which cannot quantify the stability and synchronization. FAD measures the similarity between generated and reference audio samples by calculating their distributional distance. However, the sequential yet highly structured nature of audio data presents challenges for accurate FAD computation (Yang& & Lerch 2020). Hence. FAD compresses the audio file into VGGish embeddings (Kilgour et 2018), which overly compress temporal information and limit the ability access stability and synchronization. In this paper,
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propose three novel metrics, Inner-track Rhythmic Stability (IRS), Cross-tract Beat Synchroniza- tion (CBS) and Cross-track Beat Dispersion (CBD), to evaluate these properties. Specifically, IRS evaluates the rhythmic stability of an individual audio track based on the variance of its beat inter- vals. CBS quantifies the proportion of rhythmically aligned beats by employing a sliding tolerance window method. CBD computes the timing errors between aligned beats, providing more refined measurement of beat synchronization. The combination of these three metrics with FAD enables a more comprehensive and accurate evaluation of multi-track music generation quality.
In summary, our contributions are as follows: music generation model, named Sync By incorporating both track-shared modules and track- specific modules, SyncTrrack can effectively handle common and track-specific information; 2) We design two types of cross-track attention submodules, global cross-truck attention and time- specific cross-track attention for global stability and fine-grained synchronization, respectively; 3) We innovatively propose the introduction of rhythmic consistency in the evaluation of multi-track music quality. Three metrics, IRS, CBS and CBD quantitatively evaluate rhythmic stability of an individual audio track and rhythmic synchronization alignment cross tracks; 4) Empirical results confirm that Sync'Track achieved better performance of multi-trace music generation than existing state-of -hr---rrtt--ttttte methods.
1) We propose a synchronous multi-track waveform
2 RELATED WORK
2.1 RHYTHMIC STABILITY AND SYNCHRONIZATION
Musical rhythm, as core element of musical expression, has long been focal point in both musicology and cognitive science (Mirka 2004). Rhythmic stability and synchronization serve as the temporal backbone of musical organization and the foundation for effective collaboration in multi-track music composition (Abrassart Doras 2022 Hennig 2014). Rhythmic stability and synchronization are sensitive listeners; even untrained listeners are highly sensitive to temporal fluctuations or deviations in music (Large & Palmer 2002). Temporal prediction errors caused by irregular beats or inter-track desynchronization lead to persistent violations of auditory expectations, inducing listener discomfort (Mas-Herrero et al. 20188) Consequently, rhythmic inconsistencies can disrupt auditory expectations and degrade the overall musical experience, making the maintenance of coherent rhythm a core challenge in computational music research.
2.2
AUDIO GENERATION AND MULTI-TRACK WAVEFORM MUSIC GENERATION
Audio Generation. Audio generation has rapidly progressed from early autoregressive models like WaveNet (Van Den Oord et al. 2016) and SampleRNN (Mehri et al., 2016), which generate audio sample by sample, to higher-level discrete token models and advanced generative approaches. Vector quantization method, such as VAE (Razavi al. 2019). encode audio into compact code sequences, enabling models like Jukebox and MusicGen to capture long-range dependencies and facilitate efficient decoding. Recent advances utilize diffusion models-such as DiffWave (Kong ltt al. 2020b), WaveGrad (Chen et al.] 2020), and latent diffusion frameworks like Audiol (Liu al. 2023) and AudioLDM2 (Liu et al. [2024)-to further improve audio fidelity, scalability and controllability. These techniques have since been adapted for targeting musical domain like Riffu-
sion (Forsgren & Martiros 2022). Moùsai (Schneider et al. 2024), MusicLDM (Chen al. 2024).
and Stable Audio (Evans et al. 2222a 2025 2024b).
Multi-track Waveform Music Generation. Despite these advances, most models produce a single mixed waveform without access to individual instrument tracks. This limitation hinders downstream applications for professional musicians, such as remixing, adaptive arrangement, and track-wise editing (Cano et al., 2018). Multi-track music generation aims to address this gap by generating separate stems for different musical elements. Recent approaches treat multi-track music as structured. interdependent components: for instance, StemGen (Parker al. 2024) and Jen-1 Composer YYoo et al. 2025b) use transformers and latent diffusion models to generate multiple canonical stems conditioned on prompts. Other methods, such as Multi-Source Diffusion Models (MSDM) (Mariani (et al.) 2023) and MSG-LD (Karchkhadze et al. (225). extend diffusion models to jointly model fixed variable sets of tracks within unified frameworks.
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However, current model architectures incompatible with the inherent properties of multi-trace music, as they do not separately process track suck-specifii and common information. As a result, models tend to focus inter-track differences while neglecting common rhythmic patterns.
2.3 SYNCHRONIZED GENERATION IN AUDIO RELATED DOMAINS
Video-to-audio Generation. This task seek to generate audio from video content, aiming to achieve both semantic consistency and temporal synchronization. Recent methods for synchronized video-to- audio generation primarily model alignment through rhythmic and hierarchical visual conditioning, Models like Diff-BGM (Li al. 2024b) and LORIS Yuu et al., 2023) explicitly leverage dynamic motion features to control the rhythm and timing of the generated audio, ensuring beats align with on- sereen action. Another trend involves disentangling visual semantics from rhythm using hierarchical features. For instance, VidMusician (Li et al.] (024a) employs global features for semantic style and local features for rhythmic cues, while VidMuse (Tian et al. 2025) utilizes long-short-term modeling. An alternative approach, Mel-QCD (Wang et al.) 2025), deconstructs audio into components that are more predictable from video, enabling precise synchronization control for a pre-trained generator.
In this field, the objective evaluation on generated audio is based on audio quality, cross-modal alignment and rhythm alignment (Li et al. 2024a). To investigate rhythm aligmment (referred as temporal synchronization), some works Yu et al. 2023 Li et al. (2024a) use beats coverage scores (BCS), beats hit scores (BHS) for evaluation. These metrics usually estimate beats from audio at a coarse, second-wise granularity. While they are suitable for simple audio forms, they cannot capture the precise rhythmic interactions necessary for multi-track music generation.
Accompaniment Generation. In this dmoain, models like SingSong (Donahue et al. 2023) enforce rhythmic alignment with input vocals; however, evaluations often rely on subjective listening tests rather than objective metrics. Such subjective evaluations require substantial human labor and time; thus, they are difficult to use for rapid and consistent model comparison during iterative development.
2.4 OBJECTIVE EVALUATION METRICS FOR MULTI-TRACK MUSIC GENERATION
In the area of multi-track music generation, objective evaluation metrics remain limited. The most commonly used metric for waveform music generation the Frechet Audio Distance (FAD) (Kilgour et al.) 2018), which measures the distributional distance between generated and reference audio samples. Although FAD is effective in capturing overall audio quality and similarity, it falls short in reflecting important musical characteristics such as rhythmic stability and cross-track synchronization. This limitation stems from the fact that FAD relies on compressed embeddings extracted by VG- Gish (Kilgour et al.) 2018), which significantly reduce temporal resolution. The sequential and highly structured nature of music makes accurate FAD computation challenging (Yang & Lerch 2020).
as the temporal and rhythmic information crucial for music perception is lost during embedding compression. KAD (Chung et al. 2025) improves on FAD by using a distribution-free estimator, offering more efficient and perceptually aligned metric. SongEval (Yao al., 2025a)
unbiased
addresses the limitations of objective metrics by incorporating multidimensional ratings from protes- sional musicians, capturing broader aspects of musical perception. However, these newly proposed metrics still fail to adequately capture fine-grained rhythmic issues for multi-track music.
In the domain of symbolic music generation, there have been attempts to evaluate alignment using specialized metrics (Yu et al. 2022; Dong et al.] 2018). However, these metrics are tailored for symbolic representations and cannot be directly applied to multi-track waveform audio generation due to differences in data modality and the complexity of raw audio signals.
Multi-track music demands fine-grained assessments that evaluate not only the rhythmic stability within each track but also the synchronization and interplay between different tracks. To address these gaps, we propose three novel metrics tailored for evaluating multi-track music generation: IRS, CBS and CBD. When combined with FAD, these complementary metrics provide more comprehensive and musically meaningful assessment of generation quality, capturing both audio fidelity and the intricate rhythmic relationships critical for multi-track compositions.
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VAE Exodd
Syncllack
VAE Dessla
HURGAN
Gunoianzsie
Training
Sampling
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Tradk-qwiffe medale
Tradk-dund
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Midd bolado
Oupputt blodos
repecicctation
reproientatio
Figure 2: Overall pipeline for Sync Training pipeline: We train four-trad track latent diffusion model. Each track is perturbed based on 1-th signal-to-noise ratio. The model is optimized to predict the added noise E (O, /). More details in Section 3.1. Inference pipeline: At test time, four-track latents are generated and then decoded into audio data. b. SyneTrrakk consists of input, mid, and output blocks, which contains track-specific modules and track-shared modules.
SYNCTRACK: SYNCHRONOUS MULTI-TRACK MUSIC GENERATION MODEL
In this work, propose novel multi-tras music generation model based on the Latent Diffusion Model (LDM) framework (Rombach et al.] 2022; Ho et al. 2020). By learning the underlying probability distribution of the given dataset, Trackrrac can generate multi-track music samples approximately from the same underlying distribution.
Overall Framework. As shown in Fig. 2 audio data {x"} J==1 E RT' with length T' is first encoded into latent representation in steps: audio data to mel-specttrogram using Short-Time Fourier Transform (STFT) and a Mel filter bank; 2) mell-ppectrogram to latent representation using a pre-trained Variational Autoencoder (VAE) (Kingma Welling, 2013), which is formulated as follows:
(1) where 7 and F are the temporal and frequency dimensions after compression, and C' represents the hidden dimension.
VA (SFFT&&MFF RxTxF
SyncTrack aims to learn the distribution of 2", which is difficult to transform from an easy-to- -ssmple distribution. Inspired by DDPM (Ho al. 2020), we set a sequence of data distributions perturbed by L signal-to-noise ratio levels. Sync' Track iteratively improves the signal-to-noise ratio of the perturbed data distribution from I 1, ultimately generating multi-track music when signal-to-noise ratio reaches 1. The way to improve the signal-to-noise ratio is predict the added noise the perturbed data. Specifically, given the latent representation {2*} {=*)== we obtain the perturbed data {2ī] 1S by adding noise to [ at the /-th signal-to- to-noise ratio. SyncTrack eg learns to predict the added noise:
(()) B B. where is uniformly sampled from {1, L}.
c0((=)) c0((=i)s=1
In the sampling phase, as shown in Fig we can utilize the trained Sync to approximate the distribution of 29, Then the sampled 2 decoded into audio data of s-th track by leveraging the VAE decoder and HiFi-GAN vocoder (Kong et al.) 2020a):
2 = HiFIGAN (VAEdec(3))
As shown in Fig. 2p, for track-specifi and common information between tracks, SyncTrack is designed as unified architecture incorporating both track-shared modules and track-specific modules. Next delve into details of these two types of modules.

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Track-shared modules. As shown in Fig. 3.. each track-shared module consists of the ResBlock (He) ct al. 2016), inner-track attention, global cross-track attentton and time-specific cross-tract attention. Note that the inner-track attention retains the same architecture as that commonly used in the 2D U-Net (Ronneherger et al. 2015), employing identical parameters to process the inner information of each track separately.
1) Global cross-track attention. As shown in Fig. 30)),, to achieve a globally consistent tempo across all instrument tracks, introduce the global cross-track attention submodule. We slice the representation =² E RCx1x4 XF along the temporal dimension at index and along the frequency dimension index f, obtaining zi, RC, Then we gather representations from all tracks across the 21:5 temporal and frequency dimensions, denoted by 11:111:F and aggregate this information for zi, as follows:
(4) where W W W E RC are learnable parameters in the global cross-track attention, and Attn(.) the encoder layer of original Transformer. By allowing each track to reference the information of all tracks across both time and frequency dimensions, this approach helps maintain consistent tempo across all instrument tracks.
Attiigoobal cross cross (2f,,) Rtt =f,,, W 1:S 11.,,:FF W 1:S 21T,1IF),
2) Time-specific cross-track attention. While the global cross-track attention allows for consistent tempo across the entire piece, achieving finer-grained rhythmic synchronization between tracks requires localized temporal context for proper alignment. To this end, shown in Fig. (i), we introduce a time-specific cross-track attention submodule. Taking the representation zi, E R as an example. At the same I, we gather representation from all tracks across the frequency dimensions, 11: denoted by 21,1:F and aggregate this information for zi,f E ER as follows:
Attiliiee_ross (2f,,) (zf,)) = Attn(W zf,,, Ky/K ky "1,1:Fs
=(,::)
(5)
where Wº:, W and V E R are learnable parameters in the time-specific cross-track attention.
By employing attention on other tracks the same I, time-specific cross-track attention encourages instruments to align their musical events temporally, leading to tightly synchronized onset patterns and chordal structures.
Track-specific modules. In SyncTrack, we leverage the track-specific modules to capture track- specific information, such as divergent timbre and pitch range. As shown in Fig. 3p, we design learnalle instrument prior. First, we leverage one-hot vectors V to represent different tracks. These vectors are encoded via positional encoding (Mildenhall et al. (02T)) and subsequently transformed by two-layer neural network. Finally, the embeddings of V are added to the time embedding of n. Then, we add the learnable instrument prior to the output of the first ResBlock. The final spack-specific representation is obtained from the second ResBlock.
4 METRICS FOR RHYTHMIC STABILITY AND SYNCHRONIZATION
To address the rhythmic stability and synchronization issues for multi-trace music generation, we provide three different metrics. These metrics directly capture whether the beats are synchronized across all tracks S for all samples N, offering a reproducible and interpretable way to assess multi- track music generation results across different methods.
Inner-track Rhythmic Stability (IRS). For music with a stable rhythm, beat intervals should remain consistent within each track. IRS quantifies temporal consistency by averaging the standard deviation of the Inter-Beat Interval (Dannenberg 1987 Robertson 2012) across all samples for each track s:
IRS Re,, [std(T))).
(6)
where E denotes the beat intervals for the track s in sample n, with each element defined as time difference between two consecutive beats.
Cross-track Beat Synchronization (CBS). CBS measures rhythmic synchronization among multiple tracks. Inspired by the tolerance window concept in beat tracking (Dixon) 2001), we divide the timeline into multiple time windows and compute the proportion of tracks that contain at least one beat within each window. Only tracks that contain content are considered. CBS is defined
Tim
CBS E.,
(7)
Σ
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a
Track-shared
Block
bock slantia
tack
Hez Slock
Track-preelfi medulc
Lemnaiie
attrnce
Global crassdrack attention
Bllok
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Figure 3: Illustration of the (a) track-shared module and (b) track-specific module. In (a), we leverage inner-track attention to capture the inner-track rhythmic stability and devise (c) two cross-track attention submodules capture eross-track rhythmic stability and synchronization. In (b), we construct learnable instrument prior to capture timbre and other track-specific features.
where fi.n is the ratio of tracks containing at least one beat within the i-th window, and utilize [(rri, > 0) to exclude windows where no beat occurs in any track.
Cross-track Beat Dispersion (CBD).The The CBD metric quantifies rhythmic synchronization in multi- track music by measuring the dispersion of beat alignment across all pairs of tracks. Inspired from GOTO's method (Goto & Muraoka 1997), which evaluates alignment errors between estimated and reference beats using beat error sequences, CBD extends this concept to multi-track scenarios.
We select each track as reference in and compute the beat error sequence with respect to all other tracks. For track in sample n, let by denote the (-th beat in the reference track. For each b9 we find the matching beats in the other tracks and extract еπто sequence, denoted as e(b,, The CBD metric defined as the mean or other statisties of the beat error sequence:
CBD(mcan) E.,..: [e(bn.t)]
Note that, to eliminate the influence tempo variations, we use the beat interval to normalize e(bg,,). Since the matching beats of by,, within the two intervals -I 1/2 + by, b and [bg by + 22. we divide e(b;,)) by the corresponding interval length T // 2 or T /
5 EXPERIMENTS
In this section, we conduct experiments answer the following research questions:
RQI Does SyncTrack outperform the state-of-the-an multi-track music generation methods?
RQ2 Does SyncTrack perform well in inner-track stability and cross-track synchronization?
RQ3 What are the respective contributions of the key modules to our method?
RQ4 Are the proposed metrics robust, and can they reflect human subjective preference?
5.1 EXPERIMENTAL SETTING.
Objective experiments. We conduct objective experiments on Slakh2 100 dataset (Manilow et al. 2019), following the common subset (Mariani et al. 2023) of four tracks: bass, drums, guitar, and piano. All audio files are resampled to 16 kHz and segmented into 10.2 10.24-second clips. Audio segments converted into Mel- Mee-spects for training. The model is initialized using pre-trained weights from MusicLDM (Chen et al.) 2024p and trained for 320K iterations with batch size of 16. Inference employs the DDIM sampler with 200 steps. For further details, Appendix A.5
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Subjective Experiments. We conduct web-based evaluation with two parts: mixture music assessment and individual track quality evaluation. For mixtures, we design four scoring groups, each having participants rate three audio samples comparing ground-trut sample from Slakh2100 against generated samples from SyncTrrack an MSG-LD on a 5-point scale. For individual tracks, we select two representative instruments: drums and piano, creating two scoring groups per instrument; each group presents three clips (from Slakh2100, Sync'Track, and MSG-LD) to be rated on 3-point scale. Further details are in Appendix A.6
Baselines. We choose MSDM Mariinni et al. 2023), STEMGEN Parker et al. 2024). JEN-I Composer (Yao et al. 2025b), and MSG-LD Karchkhadze al., 2025) as baselines. Our baseline selection is strictly limited to the task of multi-track waveform music generation. Models designed for stereo mixtures or symbolic music fall outside the scope of this comparison.
5.2 QUALITY GENERATED MULTI-TRACK MUSIC (RQ1)
In multi-track music generation, the most widely used metric for evaluating overall audio quality is the FAD computed on the mixture of all tracks. We evaluate our Sync on the total music generation task using the Slakh test dataset. Then, we evaluate FAD between the generated mixtures and those from the test set. As shown in Table [ SyncTrack significantly outperforms MSDM, resulting in a significant reduction in FAD scores from 6.55 to 1.26. Compared to other baselines, Sync'rrack achieves reductions of 70.70%, 68.81 38.81% % and 3.82% in FAD. This major improvement confirms that Sync'Track can generate high-quality, coherent mixture audio.
unconditional mode
Table I: FAD| scores of mixture music.
Metries
MSDM
STEMGEN
JEN-1 Composer
MSG-LD SyncTrrack
FAD
6.55
4.3
4.04
1.31
1.26
We further evaluate FAD scores for each generated track, using MSDM and MSG-LD as baselines due to the availability of their open-source code. As shown in Table 2 SyncTrack consistently outperforms the basclines across all tracks, achieving FAD reductions of 32.38%, 27.55%, 20. 30.77% and 45.59% over MSG-LD for bass, drums, guitar, and piano, respectively.
Table Track-wise FAD4 scores.
Method
Bass
Drum
Guitar
Piano
SyncTrack
0.710
0.710
1.450
1.110
MSG-LD
1,050
0.980
1,830
2.040
MSDM
6.304
6.721
4.259
5.563
The lowest FAD scores are observed for rhythmic tracks like bass and drums, which exhibit the most distinctive percussive patterns, indicating model's strength in capturing these grooves. For melodic tracks such as piano-vo with their broad pitch range and complex spectra- -iri-rrrorrro models have typically underperformed. The substantial FAD improvement (45.59%) the Piano track highlights our model's enhanced ability to model the intricate distributions of challenging tracks.
Table 3: Subjective evaluation results.
Mixture
Drum
Guitar
Method
2
3
1
2
1
2
Ground Truth
4.2-10.9
4.5=0.6
4.7:00.5
4.6:10.6
3.0=0.2
2.61:0.7
2,9:10.3
3.0=0.2
SyncTrack
3.311.0
3.5=0.8
3.010.9
3,910.9
1.9-0.3
2.110.5
1.910.4
1.8:0.5
MSG-LD
1.5:10.6
1.3=0.5
18810.9
1.710.8
1.2=0.5
1.341.6
1.2:0..5
1.240..4
The results of subjective experiments shown in Table 3
The methods achieve the following average scores: Ground truth (4.48), MSG-LD (1.57), and SyncTrack (3.42), demonstrating that music generated by SyneTrrack is better perceived by listeners.
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5.3 RHYTHMIC STABILITY AND SYNCHRONIZATION (RQ2)
In this section, investigate whether the quality improvement stems from enhanced rhythmic stability and synchronization. We evaluate the rhythmic stability (IRS) and cross-track rhythmic synchronization (CBD, CBS) of the music generated by SyncTTrack and the baselines. Only statistical values are provided here. Further visualizations are provided the Appendix A.3
Table 4: Track-wise IRS, scores.
Method
Bass
Drum
Guitar
Piano
Ground Truth
0.015
0.005
0.016
0.015
SyncTrack
0.021
0.011
0.024
0.023
MSG-LD
0.041
0.040
0.039
0.039
MSDM
0.050
0.036
0.034
0.046
Analysis of Inner Track Stability. We evaluate rhythmic stability using our proposed IRS metric, which quantifies the variance of inter-beat intervals. A stable beat is essential for high-quality music, particularly for percussion instruments like drums. As shown in Table ground truth tracks exhibit lower IRS values than all generated samples, with percussion achieving the lowest scores as expected. SyncTrrck outperforms MSG-LD and MSDM in IRS across all tracks, showing the largest improvement on drums, which highlights its superior capability in modeling rhythmic patterns. Moreover, while FAD does not capture every aspect of quality, we can still observe that lower IRS is consistently associated with lower FAD, reflecting a reduced gap between generated and real music.
Analysis of Cross-track Synchronization. To analyze the rhythmic synchronization across tracks in multi-track music generation. We employ Cross-track Beat Synchronization (CBS) and Cross-track Beat Dispersion (CBD) evaluation metrics. A higher CBS indicates more simultaneous beats, and a lower CBD reflects less dispersion, both implying stronger synchronization.
As shown in Table 5)
SyncTrrack achieves the best performance on both CBS and CBD metrics, indicating tighter rhythmic synchronization across tracks. For example, SyncTrrack attains CBS of 0.5206, which is 34.8% higher than MSG-LD (0.3861) and also outperforms MSDM (0.4694). Regarding CBD (mean). SyncTTrack reaches 0.2681, representing a 27.8% reduction compared to MSG-LD (0.3714). SyncTrack also obtains the lowest CBD (std) and CBD (median), indicating the best rhythmic synchronization across tracks.
Table 5: Cross-track synchronization metries scores.
Metrics
Ground Truth
SyncTrackk
MSG-LD
MSDM
CBS f
0.5740
0.5206
0.3861
0.4694
CBD (mean)]
0.2412
0.2681
0.3714
0.3127
CBD (std).
0.1578
0.2131
0.2642
0.2217
CBD (median).
0.2066
0.2258
0.3545
0.2811
5.4 ABLATION STUDY (RQ3)
SyncTrack is built upon the Backbone model by incorporating three key modules: @ specific module, ① global cross-track attention, C time-specific cross-track attention. Note that b and C appear sequentially within each track-shared module. We consider six ablation variants here: (1) Backbone; (2) Backbone w/ @; (3) Backbone w/ @+ (4) Backbone w/ O+O; (5) SyncTrack- reorder, which reverses the order these two attention modules ( and @: (6) SyncTrack-alternann which uses only one of b or C in each track-shared module alternatively. As shown in the Table 6 we report FAD scores for all models and the FAD improvement (Promotion) of SyncTrack ' final mixed music over its ablated variants. Our experiments address the following questions:
Are all three modules useful? Yes. SyncTrack achieves significant improvement over the backbone (50%) and variants (1 (11%-27%)

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2. Do the three modules play distinct roles? The ablation studies confirm that each of the three modules serves a distinct and complementary function. Module @ captures timbre and other track- specific features, resulting in significant improvement in individual- from 56. 18% to 84.41 1).. Module ) enhances the stability of each track. Hence, Backhone w/ @+ has significant improvement (6.3%-22.555% over Backhone w/ @ in single-track music quality. Module C enables fine-grained synchronization, aligning musical events across tracks at the same temporal position. Backbone @ shows significantly enhanced overall quality (17.97%) of multi-track music over the backbone w/ @
audio quality (ranging
Is the integration modules well-reasoned? Yes. SyncTTrack outperforms both SyncTrrck-rreocee and SyncTrack-aal Track-alternat variants, supporting the design choice of incorporating both b and ( concurrently in the proposed order. Since C enables a more fine-grained synchronization building upon b the sequence of first ( followed by C is well-founded.
Table 6: Ablation study of track-specific module and two types cross-track attention.
Model
Bass
Drum
Guitar
Piano
Mixture
Promotion
Backbone
5.234
3,081
6.012
6.170
2.570
50.97%
Backbone w/ @
0.816
0.809
2.634
1.695
1.742
27.67%
Backbone w/ @++h)
0.632
0.758
2.367
1.359
1.627
22.56%
Backbone w/ @+C
0.892
0.889
2.680
1.547
1.429
11.83%
TracTTac--aatemaaa
0,900
0.897
2,663
1.757
1.586
20.55%
SyncTrrack reorder
0.957
0.943
2.887
1.877
1.681
25.04%
Sync
0.710
0.710
1.450
1.110
1.260
5.5 ROBUSTNESS AND SUBJECTIVE VALIDATION OF EVALUATION METRICS(RQ4)
We select samples with varying objective metric scores (IRS, CBS, and CBD) and present them to human listeners for subjective evaluation. The results show clear correspondence: samples with lower IRS receive higher subjective stability ratings, while those with higher CBS and lower CBD receive lower rhythmic synchronization scores, indicating that users perceive them as less rhythmically aligned. Experimental details and questionnaire settings are Appendix A..6]
We further verify metric robustness by varying beat tracking hyperparameters; model rankings remain consistent across all settings, confirming the stability of our metrics (see Appendices A. and A.2)
CROWAR
809
9
scones
Figure 4: Comparison of subjective ratings and objective metric scores.
CONCLUSION
In conclusion, we present SyncTrack, which effectively addresses the often-overlooked issues of rhythmic stability and synchronization in multi-track music generation. By integrating track-shared modules that include cross-track attention submodules and track-specific modules that incorporate learnable instrument priors, SyncTrack is highly effective in achieving rhythmic consistency and capturing the unique characteristics of each track. Our proposed metrics -RS, CBS, and CBD- -oferr comprehensive framework for evaluating rhythmic features. Experiments show significant improve- ments in the musical and rhythmic quality of the music generated by our model. In the future, we plan to extend SyncTrack generate longer-form multi-track music and broaden its applications.
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ACKNOWLEDGMENTS
This work was partially supported by an Area of Excellence project (AoE/E-601/24-N) and Theme- based Research Project (T32-615/24-R) from the Research Grants Council of the Hong Kong Special Administrative Region, China. We also acknowledge the funding from the Hong Kong Innovation and Technology Commissionand (ITCPD/17-9). We would like to thank all reviewers for their helpful suggestions in improving this paper.
ETHICS STATEMENT
This work presents novel method in the field of machine learning. We have considered the potential societal impacts of our research. While our primary aim is to advance the state of knowledge, we acknowledge that any powerful technology could potentially be misused. We have specific reason to believe that our work poses immediate significant risks, and we encourage the responsible use of the technology developed herein. All authors of this paper declare that there no competing interests, financial or non-financial, related to this work.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide comprehensive experimental details in Appendix A.5 Audio samples, alongside with the source code for both the model and evaluation metrics, are available on demo page.
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A APPENDIX
A.1 BEAT TRACKING IMPLEMENTATION DETAILS
Tools and Default Settings. We conduct objective experiments on Slakh2100 dataset (Manilow et al. 2019). Note that we do not choose MUSDB13 18 because data sparsity would lead to a large approximation error (Zhang et al. (2255). We utilize the RNNDownBeet
and DBNDown- rocessor from the madmom (Böck et al. 2016) library for beat extraction throughout our experiments. Unless otherwise specified, we the frame rate (fps) 150Hz and used madmom's default transition lambda (tl) value (Böck et al. 2016) for all main results.
Key Parameters. Beat detection accuracy directly affects rhythm-relater related metrics. To examine the robustness of our metrics, we systematically varies two key parameters in beat tracking:
Frames per Second (fps): Higher fps provides finer temporal resolution, enabling more precise beat localization-
important for multi-track or sparsely rhythmic content. Lower fps reduces computational cost but may degrade detection accuracy.
Transition Lambda (tl): This parameter controls temporal smoothing during beat sequence inference. Higher tl enforces smoother, more consistent tempo estimation, but may mask genuine rhythmic instabilities in generated tracks. Excessively high values can artificially inflate rhythmic stability scores by obscuring local irregularities. We find the default tl offered a good balance between sensitivity and smoothing, reliably reflecting true rhythmic quality.
A.2 PARAMETER SENSITIVITY ANALYSIS
To rigorously assess the robustness of our evaluation metrics, we conduct experiments across a grid of beat tracking configurations, varying both fps and tl Figures A and A2 well Table A summarizes the effects of these parameters on our three metrics: Inner-track Rhythmic Stability (IRS), Cross-track Beat Synchronization (CBS), and Cross-track Beat Dispersion (CBD).
Across all settings, the relative ranking of models remains stable, demonstrating that our metrics are robust changes in beat tracking hyperparameters.
845
3.30
Meried
MSG-LD
6.0
Evd agr
poppoo
andrter aar
put
beth not agiaa
andrise
doup
Eugoon ael
endrior ta
wnth apto
Svarr Moarrhgph
NS2M
Figure A1: Comparison of IRS across hyperparameter settings,
Inner-track Rhythmic Stability (IRS). As shown in Figure Al the relative ordering among GT. SyncTrack, MSG-LD, and MSDM remains unchanged as beat tracking parameters vary. This demonstrates that our rhythmic stability metric is robust to beat tracking hyperparameters, and that model comparisons are reliable regardless of the specific configuration.
Besides, increasing either fps or tl results in a consistent decrease in IRS values across all methods. This trend reflectes the influence of beat tracking hyperparameters: higher fps yield finer temporal resolution, allowing for more precise and stable beat localization, while higher tl enforces greater temporal smoothing and more regular tempo estimation. Both effects reduce the measured variance of beat intervals, thereby lowering IRS scores.
Cross-track Beat Synchronization (CBS). As shown in Figure A2 CBS exhibites a consistent dependency on both beat tracking hyperparameters and the chosen window size. Varying the window size has a clear and intuitive effect: smaller window sizes impose a stricter criterion for considering beats as synchronous, leading to lower CBS scores, whereas larger window sizes relax this criterion and yield higher CBS values. This relationship holds consistently for all methods and parameter settings.
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Sudding bxg 100 RB
Setting taa 103 1g50
Seding taad 130 193
045
043
Spee
Micza
Serting twigrte kgerte
Suding bxag 98 100
Sertin
085
688
045
Windiw 3
Syertook
WOOLD
Sebing
Butting
Figure A2: Comparison of CBS across hyperparameter settings.
Despite these variations in absolute CBS values, the relative ranking among GT, SyncTraack, MSG-LD, and MSDM remains unchanged across all configurations. Ground truth consistently achieves the highest CBS, and the ordering among models was preserved regardless of the hyperparameter or window size choices. This stability demonstrates the robustness of CBS as comparative metric for evaluating model performance under diverse evaluation settings.
Cross-track Beat Dispersion (CBD). Table Al presents detailed statistics for CBD mean, std, and median. Moreover, although the absolute values of CBD metrics varies slightly with fps and the relative model ranking is almost unaffected. This further corroborates the insensitivity of our evaluation framework to beat-tracking hyperparameters.
Table A1: Comparison of CBD across hyperparameter settings.
Setting
Metrics
Ground Truth
SyncTrrakk
MSG-LD
MSDM
CBD (mean)
0.2206
0.2589
0.3644
0.2923
CBD (std) |
0.1693
0.2241
0,2822
0,2370
fps@@ 100 t0@50
CBD (median) J
0.1769
0.2058
0.3350
0.2424
CBD (mean)
0,2143
0.2522
0.3829
0.3205
CBD (std) |
0.1556
0.2101
0.2708
0.2329
fps@ 100 @@ 100
CBD (median) +
0.1762
0.2060
0.3679
0.2870
CBD (mean)
0,2412
0.2681
0.3714
0.3127
CBD (std) |
0.1578
0.2131
0.2642
0.2217
fps @ 150 @ 100
CBD (median) +
0.2066
0.2258
0.3545
0.2811
CBD (mean)
0.2642
0.2926
0.3590
0.3109
CBD (std) +.
0.1639
0.2203
0.2534
0.2134
fps@200 @ 100
CBD (median)
0.2316
0.2545
0.3407
0.2807
A.3 IMPROVEMENT IN SYNCTRACK'S RHYTHMIC STABILITY AND SYNCHRONIZATION
We examine the consistency this improvement SyncTrack's Rhythmic Stability and Synchro- nization throughout the Slakh2100 dataset.
Rhythmic Stability. We examine the rhythmic stability by computing IRS scores for Bass, Drum, Guitar and Piano. From Figure A3 and MSG-LD, demonstrating the generated music of SyncTrack closely approximates real music with greater rhythmic stability.
observe that SyncTrack achieves lowest IRS than MSDM
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Rhythmic Synchronization. To analyzes the rhythmic synchronization across tracks in multi-track music generation. We employ CBS and CBD as evaluation metrics. CBS measures the proportion of time windows in which multiple tracks have simultancous beats a higher CBS indicates stronger synchronization. CBD quantifies the dispersion of beat timings across tracks-4 lower CBD) means greater rhythmic synchronization.
As shown in Table in main text, SyncTraak achieves best performance all generative methods. We further examine the consistency of this improvement Rhythmic Synchronization, As shown in Figure A4 we can see the generated music of SyncTrrack generally outperforms that of MSDM and MSG- SyncTrack ensures superior rhythmic synchronization across multiple tracks.
Ground Pues
0.10
0.08
0.04
0.02
Bas
Dum
Plano
Tracks
Figure A3: Track- Track-wise IRS scores.
CBD (mean)]
0.4
0.2
Groad Truth
SpecTreck
MSDM
MEGL0
Groand Truih
SyecTack
MSDM
MEG-o
Methods
Methods
0.5
[median)
CBD [std]]
0.3
0.2
0.0
0.0
Groand Thuih
SyncTreck
MSDM
MSGAD
Groan Truit
SyncTrack
MSDM
MSG-LD
Methods
Methods
Figure A4: Cross-track synchronization metrics scores.
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A.4 CASE STUDY RHYTHM EVALUATION METRICS
To further validate the effectiveness and interpretability of our proposed rhythm evaluation metrics, present case study comparing both Ground Truth (GT) samples from the Slakh2100 dataset and generated samples from baseline models. We focus on three key metrics: IRS, CBS and CBD.
Inner-track Rhythmic Stability, Figure A5 showed two representative drum tracks from the GT dataset (left) and two from Daseline-generann samples (right). For each, we visualize the spectrogram, beat and downbeat activations, and the extracted beat sequence. The GT drum tracks exhibit highly regular and stable beat intervals, as reflected in both the visualized beat grid and the very low IRS values (0.0049 and 0.0051). contrast, the baseline baseline-generated tracks display irregular beat sequences, with beat intervals that fluctuate over time and substantially higher IRS values (0.1859 and 0. 0494). These results demonstrate that IRS effectively captures the stability rhythmic patterns within single track and aligned with intuitive human judgments.
Multi-track Beat Synchronization. We further analyze multi-track synchronization using two Ground Truth examples and two baseline-genenann examples, shown in Figure A6 and Table A2 The Ground Truth samples exhibit strong cross-track synchronization: beats from different instruments (bass, drums, guitar, and piano) well aligned, as visible in the vertical alignment of beat annotations across tracks. Correspondingly, both CBS and CBD metrics indicate high synchronization and low dispersion across different track rhythm.
In contrast, the generated samples display chaotic beats across tracks, and in some cases, entire tracks empty. This reflected in the lower CBS values and higher CBD values, indicating both worse cross-track alignment and larger rhythmic dispersion between tracks.
IRS
IRS- 0.18
IRSH DOOSS
IRS- 0,1494
Samples from Slnkh2100 dataset
Generated samples from baselines
Figure A5: Case study of IRS for ground truth and generated samples.
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Samples from Stakh2 100 dataset
Generated samples from baselines
Figure A6: Visualization of cross-trec track synchronization in ground truth and generated samples.
Table A2: Comparison of CBS and CBD for ground truth and generated samples.
Sample
CBD(mean)
CBD(std)
CBD(median)
CBS
①
0.0702
0.0481
0.0709
0.5286
Slakh2100
0.0628
0.0677
0.0553
0.7857
0.2619 0.3975
0.2512
0.1522
0.3629
Generated from baselines
0.2387
0.3815
0.3041
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A.5 TRAINING CONFIGURATION AND MODEL DETAILS.
During training, the audio segments are converted into Mel-spectrograms using window size of 1024 and hop length of 160 samples. The mixture audio for each clip is obtained by summing the individual tracks. The Adam optimizer is used with learning rate of 3e 5.
SyncTracck consists of 241M trainable parameters and 128M non- trainable parameters. More details of the architecture are summarized in AA3 When trained on an A6000 GPU with a batch size of 16, each epoch takes approximately 11 minutes. The full training process required under 3.5 hours (3:07:37) to complete 21 epochs.
Table A3: Network architecture of SyncTrrack
Layer (type)
depth-idx
Input Shape
Output Shape
Param
Sync
[1,4, 8, 64, 64]
128,122,516
Diffusion DiffusionWrapper
[1, 4, 8, 64, 64]
4, 64, 64]
UNetModel
2-1
[4, 16, 64, 64]
[4. 8, 64, 64]
16
Sequential (time embed)
3-1
[4, 128]
[4. 512]
Linear
4-1
[4, 128]
[12]
66,048
SiLU
4-2
[4, 512]
[12]
-Lnnear
4-3
[121
[12
262,656
Sequential (label_emb
3-2
[4. 81
14,
Linear
444
[4.
[4, 128)
1,152
SiLU
4-5
[4, 128]
[4, 128]
Linear
4-6
[4, 128]
[4, 512]
66,048
ModuleList blocks)
3-3
Track-specific Module
4-7
[4, 8, 64, 64]
[4, 128, 64, 64]
9,344
Track-speeils Module
4-8
[4, 128, 64, 64]
[4, 128, 64, 64]
426,880
spacifs Module
[4, 128, 64, 64]
[4, 128, 64, 64]
426,880
Track-shared Module
4-10
[4, 128, 64, 64]
[4, 128, 32, 32]
147,584
Track-shared Module
4-11
[4, 128, 32, 32]
[4, 256, 32,
3,681,280
Track-shared Module
4-12
25,, 32,
[4, 256, 32,
3,943,424
Track-shared Module
4-13
32.
256. 16, 16]
590,080
Track-shared Module
4-14
[4, 16, 16]
[4, 384. 16, 16]
8,323.712
Track-shared Module
4-15
[4, 384, 16, 16]
384. 16, 16)
8,667,648
Track-shared Module
4-16
[4, 384, 16, 16]
[4, 3,3,8,888
1,327,488
Track-share Module
4-17
384
14, 640,0,888
22,392,448
Track-shared Module
4-18
640.
14, 8.
23,621,120
Track-shared Module (middle)
3-4
640.
[4, 640,
Track-specific Module
4-19
640, 81
[4. 6,0,8,
8,032,640
Track-shared Module
4-20
[4, 640. 8, 8]
[4, 640, 8,
15,588,480
Track-specift Module
4-21
[4. 640. 8.
[4, 640. 8.
8,032,640
ModuleList (output.blocks
3-5
Track-shared Module
4-22
[4, 1280, 8, 8]
[4, 640, 8, 8J
28,128,66 640
Track-shared Module
4-23
[4, 1280, 8, 8]
[4, 640, 8, 8]
28, 140.6000
Track-shared Module
4-24
[4, 1024, 8, 8]
[4, 640, 16, 16
60,176,768
Track-shared Module
4-25
[4, 1024, 16, 16]
[4,384, 16, 16]
11,274,368
Track-shared Module
4-26
[4, 768. 16, 16]
[4.384. 16]
10,290,816
Track-shared Module
4-27
[4, 640, 16. 16]
[4, 384, 32]
11,126,528
Track-shared Module
4-28
[4, 640, 32]
[4, 256. 32]
4,993.024
Track-shared Module
4-29
51,, 32]
[4, 256, 32, 32]
4,665,088
Track-shared Module
4-30
[4, 384, 32)
256, 64, 64]
4,927,232
Track-specific Module
4-31
[4, 384, 64, 64]
[4, 128, 64, 64)
771,584
Track-specific Module
4-32
[4, 256, 64, 64)
[4, 128, 64, 64)
607,488
Track-specific Module
4-33
[4, 256, 64, 64]
[4, 128, 64, 64]
607,488
Sequential (conv out)
3-6
[4, 128, 64, 64]
[4, 8, 64, 64]
Group3
[4, 128, 64, 64]
[4, 128, 64, 64]
256
SiLU
4-35
[4, 128, 64, 64]
[4, 128. 64, 64]
Conv2d
4-36
[4. 128. 64, 64]
[4. 8, 64, 64]
9,224
20

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Published as a conference paper at ICLR 2026
A.6 SUBJECTIVE EVALUATION.
To comprehensively validate the effectiveness of the proposed metrics, we aime to determine whether these objective indicators reflect human subjective perception. To achieve this, we conduct a web- based subjective evaluation consisting of two distinct experiments. We recruit 23 Participants to participate in the questionnaire survey.
Rhythmic Synchronization. This experiment aims to evaluate the rhythmic syn- chronization across all tracks. We set up four scoring groups each group, we place three audio excerpts sourced from Slakh2100, SyncTrrack, and MSG-LD, with varying CBS/CBD values. These multi-track excerpts were mixed and presented to participants rated the degree of rhythmic synchronization among the instruments on 5-point scale. The survey provided detailed explanations of the definition of synchronization and the meanings of each rating from to 5.
Experiment 1:
randomized order. Participants
Experiment 2: Rhythmic Stability. This experiment investigates the correlation between the proposed IRS metric and the human perceived rhythmic stability of individual track. We select two representative instruments: drums (rhythmic instrument) and piano (melodic instrument). Single- track excerpts- the drum track the piano mack-with varying IRS values were sampled from ground truth data, generated music by our model and MSG-LD, Participants evaluate the inner-track rhythmic stability of each single-track excerpt using 3-point rating scale similar to Experiment 1.
A.7 PROPOSED METRIC'S ON MUSDBI8 18.
Ineribution
of
1-
Figure A7: Distribution of three metrics on MSG-LD, SyncTrack, MUSDBI8 and Slakh2100.
We utilize MUSDBIS (Rafi et al. 2017) to further illustrate the effectiveness of our proposed metrics. MUSDBIS 18 comprises genuine studio recordings that encompass wide range of musical styles. However, the inherent rhythmic variations and recording nuances in such real-world data can lead to less stable beat detection, which in turn results in its performance being lower than that the perfectly aligned Slakh2 100 dataset. Table A4 demonstrates that the CBS, CBD, and IRS scores MUSDB 18 are slightly lower than those Slakh2100. However,
rhythm synchronization metrics
21

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Published as a conference paper at ICLR 2026
real world music still outperforms generated music, and our results are fully consistent with this intuition. Furthermore, we demonstrate the distributions of the three proposed metrics on music from Slakh2100, MUSDB18 18 and the model generated music (MSDM, MSG-LD and SyncTraack) in Fig. A7 It can be observed that the distributions the three proposed metrics on music from Slakh21 100 better than those on MUSDB18 88. Despite this, MUSDB 18 maintains overall better harmony and rhythmic stability than music generated by both MSG-LD and SyncTraak.
Table A4: Scores on three metrics for the MUSDB18 and Slakh2100
CBST
[BD[mea))]
CBD
(eedma))
IRS
IRSDL
IRS
IRS(P)
MSG-TD
0.264
0.355
0.041
MSDM
0,469
0.313
0.222
0.281
0.050
0,036
0.034
0.046
SyncTrack
0.521
0,268
0,213
0.226
0.021
0.011
0.024
0.023
MUSDBI8
0,512
0.264
0.197
0.223
0.017
0.007
0.029
0.015
Slakh2100
0,574
0,241
0.158
0.207
0.015
0.005
0.016
0.015
A.8 PROPOSED METRICS CONDITIONAL GENERATION
CBS/CBD still apply in the conditional generation. Conditional generation refers to the task of generating the remaining tracks given a subset of known tracks, thereby completing coherent multi-track arrangement. For the four-track (bass, drums, guitar, and piano) music generation task, we test four scenarios: (1) B: generation of Bass given the other three tracks; (2) BP: generation of Bass and Piano given the other two tracks; (3) BGP: generation of Bass, Guitar, and Piano; (4) BDGP: generation of all four tracks together.
As shown in the Table AS the CBS and CBD metrics can still be computed for all these cases. Furthermore, the metric results align with intuition. Based on task difficulty, rhythm synchronization should follow BGDP>BGP>BP>> According our tests, the obtained CBS and CBD scores conform to this ordering.
Table A5: CBS and CBD metrics across different generation scenarios.
GT
B
BP
BGP
BDGP
CBS
0.5740
0.5620
0.5576
0.5259
0.5206
CBD(std)
0.1578
0.1902
0.1940
0.2118
0.2131
0.2066
0.2192
0.2188
0,2304
0.2258
(BDam))))
0.2412
0.2591
0.2579
0.2718
0.2681
A.9 PROPOSED METRICS ON DIFFERENT SEGMENT LENGTH
We examine the effectiveness of our metrics segments of lengths 10s, 30s, 60s, and full songs (261. (661.83499966 As shown in Table A6 we can see that our our metrics are in all cases. Moreover, as the segment length increases, rhythm synchronization becomes to our tests, the obtained CBS and CBD scores conform to this ordering.
prone degradation. According
Furthermore, present the distributions of three metrics for music pieces of different lengths in Figure A8 Intuitively, for short music pieces like 10s, metrics on most samples are well, but there still some poorer samples. For long music pieces, i.e. full song, the metrics are relatively stable across all samples.
Table A6: Slakh2100 scores on different audio segment length
Segment length
CBST
CBDJ,.
IRS]
mean
std
median
Bass
Drum
Guitar
Piano
10s
0,5740
0.2412
0.1578
0.2066
0.015
0.005
0,016
0.015
30s
0.5554
0.2344
0.1742
0.1922
0.0203
0.007
0.0298
0.0254
60s
0.5349
0.2471
0.1960
0.2018
0.0258
0.0121
0.0425
0.0366
full-song
0.4844
0.2660
0.2466
0.1995
0.0489
0.0273
0.0671
0.0710
22

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Published as a conference paper at ICLR 2026
Deseiaatinn Calteaar for Offieet Seg.netingge
Ditrbatee Oiferee Sepenst lergh
17%
Dnockatien Milieend for Oiflwont teguut leaghh
Seareattinn BALDvaal by Bilkred Eapweel Lengl
600
110
butioe outtr Offlared sepmeet Length
Destiaation KalPlaall Offeestt Sageodt
186
775
Figure A8: Distribution of three metries on different segment length.
A.10 THE USE OF LARGE LANGUAGE MODELS(LLMS)
In the preparation this work, the authors used GPT-4 (Achiam et al. 2023) to polish and improve the clarity of the English text. All generated content carefully reviewed, edited, and critically evaluated by the authors. The core ideas, experimental design, data analysis, result interpretation, and the final content of the paper remain the sole responsibility of the authors. The LLM served solely as a writing assistance tool and did not replace the authors' critical thinking or academic judgment.
23


Reviews:
[{"review_id": "RKlJrLFMeH", "reviewer": "Reviewer_FeMV", "summary": "This paper introduces SyncTrack, a new model for synchronous multi-track waveform music generation. The model uses an innovative architecture to tackle the often-overlooked challenges of rhythmic stability and synchronization in multi-track music. It also incorporates two types of cross-track attention mechanisms: global cross-track attention, which ensures the rhythm stays consistent across tracks, and time-specific cross-track attention, which helps align musical events more precisely in time. The authors conducted thorough experiments to demonstrate the effectiveness of their approach, showing that it performs well in creating high-quality multi-track music.", "questions": "1.Besides the FAD metric and the three proposed in the paper, are there other commonly used metrics for evaluating the quality of generated audio?\n\n2.Why didn't the authors include a user study to subjectively score the generated results?\n\n3.What is the relationship between different tracks and aspects such as rhythm and timbre? Does the model impact the quality of individual track generation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1.The modules designed by the authors are highly aligned with the current needs of music generation technologies, and their effectiveness is demonstrated from both temporal and spatial perspectives.\n\n2.The authors propose three novel metrics to evaluate the quality of multi-track music generation, addressing gaps in existing evaluation methods.\n\n3.The experiments conducted by the authors are thorough and provide strong evidence supporting the effectiveness of the proposed approach.", "weaknesses": "1.The authors do not provide a clear explanation of the relationship between tracks, timbre, and rhythm in the music generation task, which may cause some difficulty in understanding the proposed approach.\n\n2.The ablation studies are limited and the results are relatively average, making them less convincing in demonstrating the contributions of specific components.\n\n3.The paper does not include a user study, leaving the subjective evaluation of the generated music quality unaddressed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "UPK5THdLZX", "reviewer": "Reviewer_pAAM", "summary": "The paper targets a concrete missing piece in multi-track music generation: current models often produce plausible individual tracks that are not rhythmically aligned. To fix this, the authors propose SyncTrack, which (i) separates track-shared rhythm from track-specific instrument information, (ii) adds two cross-track attentions (global for overall groove, time-specific for exact same-time alignment), and (iii) introduces three rhythm-aware metrics (IRS, CBS, CBD) to evaluate stability and synchronization that FAD cannot capture. Experiments on 4-track Slakh2100 show better FAD and better rhythm metrics than recent multi-track baselines.", "questions": "Your three metrics all rely on a beat (or onset) extractor. How do you handle tracks that have very sparse or chordal content (e.g., piano pads) where the beat detector fails or produces inconsistent beat intervals? Do you ignore such tracks in IRS/CBS/CBD, or do you impute missing beats? What is the effect on CBS when one track is unreliable?\n\nThe two cross-track attention submodules are placed together in the track-shared module. Did you test alternative orderings (e.g., time-specific first, then global) or distributing them across different layers? If so, do results change significantly?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Very clear problem–solution alignment. The paper does not vaguely say “quality is low”; it pinpoints “rhythmic stability and cross-track synchronization are not modeled or evaluated,” and the proposed modules map 1:1 to that diagnosis. \n\nMetrics with reuse potential. IRS/CBS/CBD are defined in a way that any multi-track model with separated stems can use; this makes the paper more than “a new model,” it is also “a more appropriate test.” \n\nRealistic multi-track setup. Using four typical production tracks (bass, drums, guitar, piano) matches real downstream use (mixing, remixing, track-wise editing) much better than single-mix generation. \n\nEmpirical validation + robustness. The authors not only report scores but also check metric robustness w.r.t. beat-tracking hyperparameters and human subjective ratings, which strengthens the claim that the new metrics are meaningful. \n\nArchitecture that is easy to transplant. The separation between track-shared and track-specific modules is structurally simple and could be dropped into other latent-audio diffusion systems.\n\nConditional/partial generation not studied. A common real-world scenario is “I already have two tracks, generate a third one in sync.” The current setup is “generate all four together.” It would be good to see whether the same architecture can be conditioned on existing tracks and whether CBS/CBD still apply in that case.", "weaknesses": "Beat-detection dependency. All three rhythm metrics assume that beat/onset tracking works reasonably well. For highly expressive, weakly pulsed, or rubato multi-track music, tracking can fail, which would make IRS/CBS/CBD less reliable. The paper tests robustness to hyperparameters, but not to style changes; adding such a test would make the metric story stronger.\n\nDataset narrowness. Most results are on the Slakh2100 four-track configuration, which is clean and well-aligned. It is unclear how well SyncTrack and, more importantly, the metrics behave on genuine studio stems with small human timing jitter.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "iFyYuK7MSK", "reviewer": "Reviewer_CJkQ", "summary": "This paper presents SyncTrack, a latent diffusion framework for unconditional multi-track music generation designed to enhance rhythmic stability and cross-track synchronization. The model integrates global cross-track attention with time-aligned cross-track attention, along with a track-specific (instrument-prior) branch that maintains timbral distinctions across instruments. The authors further introduce three reproducible evaluation metrics—IRS (intra-track rhythmic stability) and CBS/CBD (cross-track synchronization/dispersion)—and conduct experiments on Slakh2100 using a four-track configuration (16 kHz, ~10.24 s segments, ~200 DDIM steps). The proposed method achieves improved FAD (both mix-level and per-track) compared with waveform-based baselines, and the CBS↑/CBD↓ trends align with human listening evaluations.", "questions": "1. Could the authors clarify the listening-test protocol in the main text, including details such as the panel size, number of votes per item, statistical testing procedures, and loudness normalization, to improve transparency and reproducibility?\n\n2. Could the authors provide a detailed efficiency and time analysis?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper introduces clear and reproducible rhythm-centric evaluation metrics (IRS/CBS/CBD) and provides robustness analyses, making these metrics potentially useful for broader community adoption.\n2. The results demonstrate good alignment between objective and subjective evaluations: FAD improvements are consistent with listening preferences, and component-level ablations further validate the architectural design.", "weaknesses": "1. The current setup (10.24 s at 16 kHz) constrains both long-range musical structure and high-frequency detail. Including evaluations on longer segments (≥30–60 s) or full-song contexts would strengthen the claims.\n2. Key details such as the number of participants, votes per sample, confidence intervals, or statistical significance, and loudness normalization procedures should be clearly reported in the main paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "nZt1xFfowZ", "reviewer": "Reviewer_1JZQ", "summary": "In this paper, the authors propose a model architecture where modules are connected to allow global and local cross-track attention. This resulted in improved scores in terms of FAD as well as the proposed CSB metric.", "questions": "It would be much nicer if the proposed method is discussed in more detail. For example, from Eqs. (4-5), what is the dimensions of the W variables? What is the exact operation of Eq(4), especially Attn()? These two equations are too important to be discussed on this high level only. This also raise an issue of reproducibility: I'm not sure if the paper has enough details for the readers to reproduce the model.\n\nI'm also curious and a bit concerned about the proposed metrics - CBS (mean, std, median) and CBD. Are they really ↑ to 1 and ↓ to 0? It may feel deviated from the original idea of the paper, but since these are proposed by the authors in this paper, I wonder what is the reasonable range of them, perhaps measured on real human composed and produced music signals. \n\nOn FAD, SyncTrack indeed shows the lowest FAD scores, but why? The proposed method is about time sync. Why does it lead to a lower FAD?\n\nAlso on FAD, I would like more details. What was the model or source code used to measure it?\n\nThere are minor typeos, e.g. L159 - \"estimator,offering\". L441 - \"Backbone w track\".", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The core idea of the paper of using cross-attention globally & time-specifically is simple and intuitive. Based on a holistic evaluation by FAD and the specific evaluation by IRS, the proposed idea seems to achieve the goal. The paper also presents additional experiment results with ablation and subjective tests.", "weaknesses": "The background sections can be improved. It seems to cover the music generation well, but synchronized generation is an active research topic in audio generation in the context of videos.\n\nThe proposed process can be explained in more detail. The paper covers the high-level information well, but it lacks of the detailed operations.\n\nIt is nice to see a new proposal on evaluation metrics, but it needs more research and experiment to justify them.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)