SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation
Authors: Sifei Li
Yang Li
Zizhou Wang
Yuxin Zhang
Fuzhang Wu
Oliver Deussen
Tong-Yee Lee
Weiming Dong
Venue: ICLR 2026 Poster (2026)
Keywords: conditioning method
controllable song generation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A cover song generation framework and a novel conditioning method

Abstract: Cover songs constitute a vital aspect of musical culture, preserving the core melody of an original composition while reinterpreting it to infuse novel emotional depth and thematic emphasis. Although prior research has explored the reinterpretation of instrumental music through melody-conditioned text-to-music models, the task of cover song generation remains largely unaddressed. In this work, we reformulate our cover song generation as a conditional generation, which simultaneously generates new vocals and accompaniment conditioned on the original vocal melody and text prompts. To this end, we present SongEcho, which leverages Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), a framework that incorporates controllable generation by improving both conditioning injection mechanism and conditional representation. To enhance the conditioning injection mechanism, we extend Feature-wise Linear Modulation (FiLM) to an Element-wise Linear Modulation (EiLM), to facilitate precise temporal alignment in melody control. For conditional representations, we propose Instance-Adaptive Condition Refinement (IACR), which refines conditioning features by interacting with the hidden states of the generative model, yielding instance-adaptive conditioning. Additionally, to address the scarcity of large-scale, open-source full-song datasets, we construct Suno70k, a high-quality AI song dataset enriched with comprehensive annotations. Experimental results across multiple datasets demonstrate that our approach generates superior cover songs compared to existing methods, while requiring fewer than 30% of the trainable parameters. The code, dataset, and demos are available at [https://github.com/lsfhuihuiff/SongEcho_ICLR2026](https://github.com/lsfhuihuiff/SongEcho_ICLR2026).

Published as a conference paper at ICLR 2026
SONGECHO: TOWARDS COVER SONG GENERATION VIA AESTANCE-ADAPTIE WLEMENT-WISE LINEAR MODULATION
Sifei Lit2, Yang Li Zizhou Wang', Yuxin Zhang Fuzhang Wu', Oliver Deussen', Tong- Yee Lee², Weiming Dong!2.
MAIS, Institute of Automation, Chinese Academy of Sciences
School of Artificial Intelligence, University of Chinese Academy of Sciences
$ISRC, Institute of Software, Chinese Academy of Sciences
UUniversity of Konstanz
National Sheng-Kung University
flisifei2022, yangl12022,
ABSTRACT
Cover songs constitute a vital aspect of musical culture, preserving the core melody of original composition while reinterpreting it infuse novel tional depth and thematic emphasis. Although prior research has explored the reinterpretation of instrumental music through melody-conditioned text-to-music models, the task of cover song generation remains largely unaddressed. In this work, we reformulate our cover song generation as conditional generation, which simultaneously generates new vocals and accompaniment conditioned on the original vocal melody and text prompts. To this end, we present SongE- cho, which leverages Instance-Adaptive Element-wise Linear Modulation (IA- EiLM),, a framework that incorporates controllable generation by improving both conditioning injection mechanism and conditional representation. To enhance the conditioning injection mechanism, we extend Feature-wiss wise Linear Modula- Element-wise Linear Modulation (EiLM), facilitate pre-
tion (FiLM) cise temporal alignment in melody control. For conditional representations, we propose Instance-Adaptiee Condition Refinement (IACR), which refines condi- tioning features by interacting with the hidden states of the generative model, yielding aatannee-ddative conditioning. Additionally. address the scarcity of large-scale, open-source full-song datasets, we construct Suno70k. a high- quality Al song dataset enriched with comprehensive annotations. Experimen- tal results across multiple datasets demonstrate that our approach generates su- perior cover songs compared existing methods, while requiring fewer than 30% of the trainable parameters. The code, dataset, and demos are available at http:////ttttt...oooooooaaooo//r
ICLR2022
INTRODUCTION
If great melodies merit reinterpretation, then exceptional cover songs breathe new life into their orig- inals. Cover songs play an essential role musical culture, acting as conduits for cultural memory and agents in the formation of musical canon. Iconic examples, such as Whitney Houston's 's trans- formative rendition of Dolly Parton's 's "I Will Always Love You", reinterpret the style of the song, Given the expressive
evolving a gentle country ballad into worldwide anthem of deep affection [ potential of musical reimagination and cultural significance, we think that cover song generation is field worthy of exploration.
Similar to Whitney Houston's rendition, musicians creating cover songs may introduce flexible adaptations in local musical elements, such phoneme durations, vibrato, and note transitions,
"Corresponding author.
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yielding highly varied and personalized reinterpretations. In this work, we abstract cover paradigm applicable to arbitrary songs and reformulate our cover song generation as a conditional genera- tion task that performs global style transfer with text guidance while preserving the source vocal melody contour and excluding local customized adaptations. Specifically, the task requires a model to leverage the provided vocal melody as foundation structure, while concurrentyy synthesizing vo- cal and harmonious accompaniment that aligns with a given text prompt. Although text-to-song gen- eration has advanced considerably, the task of cover song generation remains largely unaddressed. The primary challenge lies in devising a model that can implicitly disentangle vocal components, ensure temporal melody control and lyric synchronization, and produce coherent accompaniment. Neglecting these elements may result in misaligned lyrics, inconsistent melodies, or degraded audio quality, thereby necessitating robust capabilities in collaborative generation and content control.
This task differs from Singing Voice Synthesis (Liu et al. 2022 Cui et al., 2024 Zhang et al., 2024a) and Singing Voice Conversion (Ferreira et al., 2025 Jayashankar et al. 2023), which deal with single-track vocals and focus on short audio segments (5-20s) that can be concatenated to produce longer audio. In contrast, cover song generation simultancously synthesizes vocals and accompaniment, necessitating coherence of the accompaniment across the entire song.
Recent works (Wu et al., 2024] Ciranni et al. 2025 Tsai et al. 2025) have achieved melody control in pretrained text-to-muse music models, demonstrating potential applicability to cover song generation. The core difference among these methods lies in their melody condition injection mechanisms, em- ploying either cross-attention (T'a et al. 2025) element-wise addition (Ciranni et al.) 2025 Wu
iet al., 2024) (see Figures [aa and [b)). Cross-attention mechanisms require extra modeling of temporal alignments, which inherently indirect and introduces computational redundancy across potentially misaligned dimensions. Element-wise addition leverages the temporal correspondence between sequences but limits modulation flexibility, acting as an affine transformation with fixed scaling factor. Beyond these limitations in condition injection mechanisms, existing methods inde- pendently encode melody conditions, thereby failing to provide targeted adaptation to the generative model's 's hidden states. Consequently, incompatible condition vectors may distort the hidden states during condition injection, resulting in unnatural and sow-fidelity audio synthesis.
To address the aforementioned challenges, we present novel framework, SongEcho, for cover song generation built upon a text-to-song model (Gong et al. 2025). We propose Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), which comprises Element- Linear Modulation (EiLM) and Andative-Adaptive Condition Refinement (IACR). These components enhance control- lable generation by refining the condition injection mechanism and conditional representation. (1) Injection Mechanism: Feature-wise Linear Modulation (FiLM) (Perez et al. 2018) has demonstrated efficacy conditioning technique. [Birbbaum al. (20T9) proposed TFiLM, which temporally ap-
plies FiLM by partitioning sequences into blocks and using an RNN (Elman 1990, Graves] (2012) to
recurrently generate block-wise modulation parameters. In contrast, we extend FiLM EiLM (see Figure [c)), which generates modulation parameters matching the target dimensions in a single operation without temporal dependency. This design enables element-wiss modulation of hidden states, ensuring the temporally aligned injection of melody. (2) Conditional Representation: We introduce the IACR module to rectify the rigidity of traditional condition encoding. By enabling in- teraction between hidden states and external conditions, IACR dynamically adapts conditions to the hidden states, mitigating feature conflicts and audio quality degradation caused by static condition injection.
Our contributions can be summarized as follows:
We introduce SongEcho, a purfmeter-efffcient framework that enables cover song gener- ation by leveraging a novel conditioning method that achieves fine-grained control of the vocal melody.
We propose Instance-Adaptive Element-wise Linear Modulation (IA-BiLM), which com- prises the EiLM and Instance-Adaptiin Condition Refinement (IACR), enhancing the con- dition injection mechanism and conditional representation, respectively.
To address the lack of open-source, high-quality, large-scale full-song datasets, intro- duce Suno70k, an open-source Al song dataset enriched with detailed annotations, includ- ing enhanced tags and lyrics.
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Block
Block
Block
K
Block
Block
(d) Cruss-Attention
(b) Element-Wiss Addition
(4 BiLM (Ouns)
Figure 1: Differences between other condition injection mechanisms and our approach. EiLM eliminates the need for separate learning of temporal alignment in (a) while offering more flexible modulation than (b). "#" represents element-wise addition, and "O" represents element-wiss mul- tiplication.
Experimental results demonstrate that our method generates superior cover songs, outper- forming tfte-off-the-n approaches across all metries on multiple datasets.
RELATED WORK
Text-to-Song Generation. Jukebox (Dhariwal et al. 2020 pioncered song generation. In cent years, industry tools such as Sund Udid Seed-Music (Bai et al. 2024), and Meruka have shown promising results in this domain. Academic efforts have followed closely, with language model-based song generation approaches, including Melodist (Hong et al. 2024), Melody eLi
2024a), Songcreator (Lei al. 2024), YuE (Yuan al. 2025). SongGin (Liu al.), and Ve (Lei et al. 2025), which autoregressively generate song tokens but require significant inference time. Diffusion-based methods, such as DiftRhythm (Ning et al. a (0025) and ACE-Step (Gong al.. 2025). have substantially reduced this latency. Notably, ACE-Step (Gong et al. (025) improves upon DiffRhythm (Ning et al., 2025) by incorporating song structure understanding. Although current models generate high-quality songs and some support audio prompts (Yuan et al. [025 Lei et al.) 2025), they lack the capability for precise temporal melody control. Considering both inference speed and performance, we adopt ACE-Step our base model.
Singing Voice Synthesis (SVS) Zhang et al. 2023b Liu et al. 2022] 7Zhang et al.] 2024a 2025) and Singing Voice Con- version (SVC) (Lu et al. 2024] Chen et al. [024]] Ferreira et al. 2025 Shao et al. (025) has led to significant progress in generating high-quality, controllable single-track vocals. Nonetheless, these approaches are inherently limited as they do not address the generation of instrumental accompani- ment. This work, in contrast, tackles the more holistic problem of full-song generation, requiring the simultaneous synthesis of a vocal track and its coherent accompaniment.
Conversion.
Extensive research in Singing Voice Synthesis
Controllable Music Generation. Recent work has advanced controllable music generation by in- corporating temporal conditions into text-to-music models using various approaches, such as In- attention (Lan et al. 2024), CostrolNet-style addition (Wu al.) 2024 Ciranni al. 2025 Hou et al. 2025). and cross-attention (Tsai et al.] (2025] Lin et al.) [2024] Yang et al.) 20255. 5). However, these dominant paradigms exhibit significant trade-ofts: additive methods offer limited modula- tion flexibility, while aross-attention is indirect and computationally redundant. Critically, all these approaches encode the condition in isolation, lacking a mechanism dynamically adapt the con- ditional signal to the generator's internal hidden states. In contrast, our approach addresses the aforementioned issues by improving the condition injection mechanism and enhancing conditional representations.
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Decoder
Out-Prooector
yyiic
Lyries
X
FFN
X.V
Ar"
IA-BILM
Linear Layer
Cruss-Attn
IACR
Tags
x2
Self-Attm
RelU
Tanh
Tanh
Timestep Embedder
Convidlayer
Tranfformer Block
Timestep
In- Projecctt
Melody Encoder
Liook Lage
Liner Layer
Linear DIT
Pitch Extractor
- wp
Deep Compression AutoEncuder
10
(100
102
We employ Diffusion Transformer (DiT) as the song generation backbone with a novel conditioning method, "IA-EiLM", for vocal melody control. Pitch Extractor and Melody Encoder extract melody features, denoted as "m". The IA-EiLM module, integrated into each Transformer block, comprises two components: IACR and EiLM. "IACR" facilitates interaction between "m" and hidden states "h,", refining melody condition, while "EiLM" modulates "h," into "hm"" with modulation parameters "xx" and "3,", derived from refined melody condition.
Figure 2:
Conditional Normalization. Conditional Normalization methods a powerful class of techniques that inject information by learning the parameters for an affine transformation network's 's inter- mediate features. Unified by frameworks like FiLM (Perez al.
2018). this approach has been
highly successful in wide range of domains, including image style transfer (Dumoulin et al. 2017 Huang & Belongie 2017), semantic image synthesis (Park al. 2019) speech recognition (Kim et al., 2017), modern text-to-image models (Peebles & Xiel [023) ), text classification (Birnbaum al. 2019), and black-box audio effect modelling Comunità et al. (2023). However, its application
to music remains unexplored, and our work investigates its potential in this domain.
METHOD
We propose SongEcho, parameter-efficient framework for our cover song generation, built upon the full-song generation model ACE-Step (Gong et al., (025) and leveraging the Instance-Adaptive Element-wise Linear Modulation (IA-BiLM), as illustrated Figure 2 We start by introducing IA-EiLM and then describe our particular model for cover song generation.
3.1 ELEMENT-WISE LINEAR MODULATION (EILM)
Unlike prior temporally controllable music generation methods Wu et al. 2024 Tsai et al. 2025
Hou et al., 2025 Yang et al. 2025), which rely on cross-attention or element- wise addition, explore the application of FiL.M (Perez et al. 2018) for melody injection and extend it to EiLMM.
Let E BxTxM denote condition feature, where B the batch size, T is the sequence length, and M is the condition dimension. Let h; E BxTxD, represent the hidden states of the i-th layer of the generative backbone, where D; is the number of feature dimensions in the layer. We aim to learn mapping function that modulates h; using to generate cover song. Feature-wise Linear Modulation (FiLM) is an effective conditioning method that has not yet been applied to controllable music generation. To enable precise temporal control, we propose Element-wise Linear Modulation (EiLM) as an extension of Feature-wird wist Linear Modulation (FiLM). This conditional modulation method dynamically adapts hidden states melody conditions through time-varying
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affine transformation. The overall modulation is defined as:
h = EiLM c) = 7/6 @ h4 + B
(1)
(7
(2)
where Nr. 3g BBXTTD, are the modulation parameters, derived from e via a linear projector ft. hith E RBBxTDD; is the modulated hidden states. Our EiL.M generalizes FiLM (Perez et al. 2018)
by generating modulation parameters that precisely match the shape of the hidden states.
3.2 INSTANCE-ADAPTII CONDITION REFINEMENT (IACR)
In addition to external improvements to the condition injection mechanism, we propose a con- dition refinement strategy, termed Instance-Adaptive Condition Refinement (IACR), which adap- tively refines the condition vector based on the hidden states of the generative backbone for im- proving conditional representations. Our IACR module employs gating mechanism adapted from WaveNet (van den Oord et al. 2016), where we enable cross-modal interaction between two branches. Beyond merely encoding the conditional input, our method ensures that the conditional features dynamically adapt to the hidden states. Specifically, a vocal pitch sequence E BxxxXx is first processed by melody encoder to produce melody features interactively refined with the hidden states h; E BxTxx,, via a gating mechanism (Van den Oord
RBxTTM x.M are then
al. 2016), denoted as:
h1 = Ln,((h))) mi m²= (m) G = tanh(h)) @ tanh(m²),
(3)
(4)
where Tr,, Lmn, denote linear layers, N₁ BxTxMM and m4 E RRBxTxM. The refined condition c E Bx dynamically adapts the current generative instance, enabling selective integration and interpretation of the melodic features.
Why is IACR necessary? To the best of our knowledge, existing control injection methods de- rive conditional features solely from the conditional input, overlooking their compatibility with the generative model's hidden state. We take our EiLM as an example to demonstrate the necessity of IACR.
In text-to-song models, the hidden states are not a blank canvas. Formally, a hidden state co(trog:,, BxTxx, conditioned an intrinsic melodic structure My. The goal of conditioning is to modulate with parameters (y, 3) such that the melody of the output, M, z (y h + B), where Fm is a hypothetical melody encoder that extracts melody from the hidden states and the target melody M. is derived from melody feature m.
text prompt trags lyrics I, and timestep already embeds
A conventional static conditioning approach generates modulation parameters solely from the melody feature m follows:
(1m.3 = F(m),
(5)
where F denotes the conditional mapping function. The optimization problem can be formulated
(Tm 3m) = arg min [m o 33) - Mall
(6)
7.
Given Th. # in our task (Ym loses timbre and lyrics), without access to the hidden states h or their intrinsic melody Ma, the transformation network T must learn universal mapping A AM, M, across all possible h, causing Equation to be underconstrained.
In contrast, our cestance-adaptive conditioning approach, implemented in the IACR module, com- putes the parameters based on both m and as:
(Th,m 3p.m) F(m,h).
By providing the network F with direct access to h. the task is transformed into one-to-one map- ping problem. In this context, Th.m and Bt.m. encoding both melodic conditions and hidden states, expand the conditional representation space. Tailored to hidden states, these conditions enable seam- less integration into the generative model, thereby improving melodic control and audio quality (see in 5.4).
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3.3 SONGECHO
Our proposed framework, SongEcho, extends the pre-trained text-to-song model, ACE-Step. by in- corporating melody encoder, &, and integrating IA-EiLM module into each transformer block. Given a vocal pitch sequence p IR. Bx,xxT"x1 extracted at 100 Hz via RVMPE (Wei al. 2023). E, comprising ID convolutional layers, encodes features as:
m E(),, m IR BxTxXxX
(8)
These are interpolated to align with the hidden states h; E BBTxxD, given by:
m Interpolate(m"), m E BBTXXM
(9)
The features m are then refined via State-Adaptive Condition Refinement (IACR) as follows:
C2 IACR(m, hi), m E B
(10)
Similar to Zhang et al. (2023a) . to mitigate noise modulation in the hidden states caused by randomly initialized parameters, we initialize f. with zeros to ensure training starts from the original model. To incorporate zero initialization, we reformulate EiLM as follows:
Mil.M-Zerr(hy/40 = (7 + 1) @ 14 + 3,
(11)
(,,, f((e)-
(12)
Given that self-attention facilitates global information interaction across tokens, while the FFN layer performs localized feature transformations, each Transformer block to inject melody information and prevent its dilution within the global attention mechanism, as illustrated in Figure 2
insert the IA-EILM module before the FFN layer
Except for & and the IA-BiLM modules, all model parameters are frozen. The training objective is defined
CIM
[21 h,,p) (-or) 21) Nu
(13)
where zo denote the latent representation, 22 (1 og tz) Since we do not update parameters related to semantic alignment, we disable the semantic alignment loss based on self-supervised learning models (Li al. 2024c Zanon Boito et al. 2024). Overall, our proposed method introduces vocal melody control in lightweight manner. A detailed comparison is provided in Table |
4 DATASET
Due to copyright constraints, the availability of publicly accessible song datasets (Hsu & Jang 2009 Bertin-Mahieux et al., 2 Zhang et al., 2024b; Yao et al.) 2025) remains significantly restricted (see details in the Appendix A.5). To address these limitations, we introduce Suno70k, high- quality AI song dataset derived from the Suno.ai Music Generation dataset [ This open-source collection contains metadata, including song links, for 659,788 Al-generated songs, but the quality
varies widely. Our curation process involves several steps:
1. Data Filtering, We filter the dataset based on metadata, removing entries with incomplete infor- mation (e.g., missing IDs, lyries, or tags) and deduplicating by ID. We exclude purely instrumental tracks and entries with unclear lyric structures, unrecognizable characters, non-English lyrics. To align with the 4-minute generation limit of the ACE-Step (Gong et al. 2025), we exclude all samples with duration exceeding this threshold.
Quality Assessment. We download the corresponding audio files and evaluate them with SongEval (Yao et al. 2025) across five dimensions: overall coherence, memorability, naturalness of vocal breathing and phrasing, clarity of song structure, and overall musicality. Samples scoring below 3 (out of 5) in any dimension are excluded.
Enhanced Tagging. Observing that the tags from metadata are incomplete, we employ Qwen2- audio (Chu et al. 2024 to generate comprehensive tags across the following aspects: genre, vocal

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type, instruments, and mood. These are concatenated with the original tags, deduplicated, and limited to 20 tags per song, separated by commas, consistent with the official examples of ACE- Step (Gong et al. 2025).
In the end, we obtain a total of 69,469 songs, with 69,379 for training and 90 for testing, yielding a total duration of approximately 3,000 hours.
EXPERIMENTS
5.1 IMPLEMENTATION DETAILS
We employ our IA-EILM on the open-source text-1o-song model ACE-Step (Gong et al. 2025), a Linear Diffusion Transformer (DiT) (Xie et al.] 2025) capable of generating high-quality songs efficiently. We freeze the parameters of the Linear DIT. lyric encoder, and text encoder, training only the IA-EiLM and Melody Encoder parameters. The learning rate is set to le-4 with a linear warm-up over 1,000 steps. We utilize the AdamW optimizer with B1 = 0.9, B2 0.95, and weight decay of 0.01. The maximum duration for music generation is set to 240 seconds, consistent with ACE-Step. Experiments
conducted on three NVIDIA A100 GPUs for 30,000 steps with batch size of 12 (1 per GPU with a gradient accumulation factor of 4).
5.2 EVALUATION METRICS
We develop comprehensive evaluation protocol that includes the following metries. For melody control, we extract melodies from ground-truth and generated songs and compute three metrics us- ing the mir_eval library (Raffel al.] 2014): Raw Pitch Accuracy (RPA), the fraction of melody frames with pitch within half semitone of the reference; Raw Chroma Accuracy (RCA), pitch ac- curacy ignoring octave; and Overall Accuracy (OA), the fraction of all frames correctly estimated, including pitch and voicing (melody vs. non-melody) alignment. Additionally. fd to calculate F Dopenis (Cramer et al. 2019 K Lpaset (Koutini et al. 2022), and CLAP score (Wu al.) 2023). We use Doppntt and K Lpsst to assess differences between the generated music and the ground-truth distribution. The CLAP score evaluates consistency between the gen- erated songs and their corresponding text tags. Furthermore,
adopt the open-
source code
compute the Phoneme Error Rate (PER) using Whisper (Radford et al. 2023) to evaluate the vocal content of the generated songs.
5.3 COMPARISON WITH STATE-OF-THE- METHODS
We compare our method with two sfacc-of-the-a melody-guided music generation approaches: Sta- ble Audio (SA) ControlNet (Hou et al., 2025) and MuseControlLite (Tsai al. (025).. The former integrates ControlNet (Zhang et al., 2023a) into the DiT-based music generation model Stable Au- dio (Evans et al. 2024). while the latter employs the IP-adapter concept enable melody control for Stable Audio. As both methods support only instrumental music generation, we apply them to the same base model, ACE-Step, used in our approach, and ensure consistency in the melody encoder. Since integrating ControlNet with ACE-Step requires over 80 GB of GPU memory at a batch size of we adopt LoRA (Hu et al. (0022) fine-tuning for subset of the copied branches, maximizing train- able parameters with a rank of 512. For reference, we also evaluate the performance of the original model. The number of trainable parameters for the three methods is shown in Table Our method significantly reduces the trainable parameters, accounting for only 3.07% of ACE-Step+SA Con- trolNet, 14.8% of ACE-Step+SA ControlNet+LoRA and 26.0% of ACE-Step+MMuseContrlll
parameters. Additional aesthetics evaluation of the comparison results is provided in Appendix C.3
5.3.1 QUANTITATIVE EVALUATION
Objective Evaluation.
The performance of our method on the Suno70k test set is shown in Ta- Our approach achieves superior results compared with the baselines. Notably, demon- strates clear advantage in Raw Pitch Accuracy (RPA) and Raw Chroma Accuracy (RCA). For the FDopenl3 metric, our method achieves reductions of 57.6% and 41.6% compared to ACE-Step+SA
ble [
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Table 1:
Quantitative evaluation results Suno70k test set. "TP" represents trainable parame- ters. The best results are in highlighted bold and the second best ones are underlined (same in the following tables).
RPA
RCA
f
CLAP
FD
KL
PER
TP L
ACE-Step (Gong et al. 2025
0.2930
73.53
0.2670
0.4168
ACE-Step+SA ContrciNet Hou et al 2025
0.6209
0.6440
0.6858
0.2875
105.95
0.2019
0.3714
L6B
ACE- ACE--iep+SA ComtrolNet-LokA Hou al. 2025
0.6214
0.6431
0.6833
0.2892
99.19
0.1850
0.3734
331M
ACE-Stop-MuseConnrod (Taai et al 2025
0.5205
0.5346
0.5940
0.2977
72.04
0.2151
0.4194
189M
SongEccoo (OurS)
0,7080
0.7339
0.6952
0.3243
42.06
0.1123
0.2951
49.1M
ControlNet+LLoke and ACE-Step+ Mus-Montrollll....ooo .ite, respectively, highlighting its effectiveness in optimizing music quality while achieving melody control.
Table 2: Quantitative evaluation results
Suno70k test set with swapped tags.
RPA
RCA
OA
CLAP
FD
KL
PER
ACexSStep ( Goog et al.
2025)
0,2800
70.54
0.3478
0.3899
ACE-Step+SA ControlNet (Hou el 2025
0.6078
0.6336
0.6759
0.2477
110.73
0.2479
0.3874
ACE- ACE-SttepSA ControlNer+LooR. A ( ala 2025
0.6143
0.6361
0.6741
0.2536
97.60
0.2407
0.4114
- (Tsar
0.5164
0.5275
0.6025
0.2462
68.73
0.2764
0.4758
SongEEho (Our3)
0.7066
0.7333
0.7001
0.2674
40.37
0,2117
0.3091
In addition, we conduct experiment involving random swapping of text tags in the test set, with results shown in Table 2 Our method consistently outperforms the other two approaches, with melody-related metries remaining comparable to those before tag swapping. The CLAP score of our method is 0.0126 lower than that of the original model, which is reasonable since the melody of song implicitly encodes certain stylistic attributes. This also explains why method outperforms the original model in terms of CLAP score in Table
Table Quantitative evaluation results on SongEval Yao al.
2025).
RPA
RCA
OA
CLAP
KL. |
PER
ACE-Step (Gong et 2025))
0.2590
71.56
0.3305
0.4510
ACE-Step+SA ControiNet (Hou et a. 2025
0.6463
0.6600
0.6934
0,2666
114.18
0.4069
0.5234
ACE-Step+SA ControlNee++oR.. Hou et di 2025
0.6335
0.6465
0.6837
0.2583
104.76
0.3112
0.5901
ACE- ACE-Step=MMuseCottool
( (Tsa al. 2025
0.5421
0.5498
0.6208
0.2600
90.19
0.3913
0.5760
SongEcho (Ours)
0.7164
0.7326
0.7097
0,2824
$1.98
0.1933
0,4487
We enhance the annotation of the publicly available SongEval (Yao al. 2025 benchmark and
compare our method with other approaches on this dataset. SongEival comprises 2,399 complete Al Al-generated songs used to train song aesthetic evaluation model, exhibiting significant variability in audio quality and lacking lyrics or tag annotations. We select the top 100 English songs with the highest aesthetic scores for testing. Corresponding music tags are generated using Owen2- audio (Chu et al. 2024). Lyrics transcription files are obtained using Whisper (Radford et al. 2023) combined with All-in-One (Kim Nam (023).. Six songs yield text unrecognizable by ACE ACE-Step, resulting in a final test set of 94 songs. The evaluation results, as shown in Table 3 demonstrate that our method achieves superior performance compared with the baselines. The observed decline in PER may result from punctuation errors (c.g., run-on sentences and incorrect sentence breaks) in transcribed lyrics, disrupting their inherent alignment with the melody (see details in Appendix C.2
Subjective Evaluation. For the subjective evaluation, we conduct a Mean Opinion Score (MOS) listening test. Specifically, we randomly select 15 groups, totaling 45 songs, as our evaluation set, each accompanied by the original song and the text prompts for the cover songs. Participants asked to rate the songs on a scale from 1 to across four dimensions: Melody Fidelity (MF), Text Adherence (TA), Audio Quality (AQ), and Overall Preference (OP). A total of 33 participants, comprising 15 with a music-related background and 18 without, take part in the evaluation. The average scores for each method are shown in Table 4 Our approach achieves the highest scores
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Table 4: Mean opinion scores (1-5) comparing melody fidelity (MF), text adherence (TA), Audio
Quality (AQ) and overall preference (OP).
w/ Music Background
wlo Music Background
MF
TA f
AQ 1
OP
MF1
TA 1
AQ 1
OP1
ACE-Step+SA ControlNet-LoRA (Hou et al. 2025
3,056
3,285
3.085
3.104
3.133
3.636
3.182
3.160
ACE:Step+Muueeonntee Cittrolllee (Tsai et al. 2025
2.630
3.026
2.581
2.622
2.689
3.333
2.591
2.622
ScngEcho (Ouis)
3.644
3.800
3.756
3.819
3.884
4.160
3,916
3.942
across all four aspects in both groups, demonstrating superior alignment with human perception compared to baselines.
5.3.2 QUALITATIVE EVALUATION
The comparison results of our method and other approaches are available at https:// vvanonynousvy, github.... Sow.eee/eoooooooooeeeeee
Our model achieves high-quality cover song generation under precise vocal melody control. Overall, the other two methods (Hou) ct al. 2025 Tsai et al. 2025) can leverage the text control capabilities of ACE-Step to achieve some extent of adaptation, but their audio quality is noticeably inferior to ours. In terms of melody control, CCEE-tept-MuseCnntroll
(Tsai et al., 2025) exhibits noticeable noise and melody drift, along with misalignment between vocals and the original audio, likely due to the cross attentooo mech- anism's failure to establish precise temporal alignment. ACE-Step+SA ControlNet+LoRA (Hou eet al. 2025) achieves decent melody control, but alignment between vocals and melody occasion- ally falters, and the integration of vocal tracks with the accompaniment lacks coherence. We also provide results on Tag-Melody Conflict, Inpainting Outpainting, and Global Tempo & Key Con- trol on the demo page, We observe that the model prioritizes the source melody when the provided style tags conflict with the melody condition. Our method supports inpainting and outpainting via a simple masking strategy. Additionally, our method allows direct control over global tempo and key by performing simple post-processing on the extracted vocal melody (FO) sequence. Arbitrary tempo changes are achieved via time-stretching, while key transposition is realized through pitch shifting.
Table 5: Ablation study of our method. "w/ EA" represents replace EiLM with element-wise ad- dition, and "IA-EiLM- indicates that insert our IA-EiLM module before the self- attention layer in each Transformer block.
RPA
RCA 0
OA
CLAP f
FD J
KL 4
PER |
w/ EA, w/o IACR
0.6336
0.6476
0.6683
0.3014
73.83
0.1689
0.3276
w/ EiLM, w/o IACR
0.6799
0.7000
0.6793
0.2999
75.28
0.1569
0.3166
IA-EiLM Self-AAttt
0.6190
0.6429
0.6303
0.3195
47.34
0.1434
0.3462
100 Training Samples
0,4677
0.4889
0,4812
0.2854
71.85
0.1402
0.4159
1000 Training Samples
0.6505
0.6775
0.6559
0.3115
48.59
0.1135
0.2871
SongEcho (Ours)
0.7080
0.7339
0.6952
0.3243
42.06
0.1123
0.2951
5.4 ABLATION STUDY
We conduct series of ablation experiments to demonstrate the effectiveness of our method. First, we replace our Eil module with diemmettwwise addition and remove the IACR Module. The results, presented in the 1st and 2nd rows of Table 5 show that the EiLM module improves melody metrics while maintaining comparable performance other metrics. Building on the 2nd row, incorporating the IACR module yields our final version. The results indicate that the IACR module not only enhances melody metrics but also substantially improves audio quality metries, underscoring the critical role of adaptively adjusting melody features based on the hidden states of the generative model for harmonious integration of melody conditions.
In our final version, the IA-EiLM module is integrated before the Feed-Forward Network (FFN) layer in each Transformer block. Compared to integrating it before the Self-Attentio layer, this

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placement results in better melody metrics. This is likely because Self-Attention performs global in- formation interaction, which may disrupt melody preservation, whereas the FFN layer only conducts local transformations, preserving the injected melody features.
We also investigate the impact of training data scale. Training with only 100 samples proves insuf- ficient for effective melody control. However, increasing the training sample size to 1,000 markedly improves performance, with some metrics approaching those achieved with our full dataset. This indicates that our method highly data-efficient and demonstrates strong potential for application in limited-data scenarios.
5.5 DISCUSSION AND LIMITATIONS
Although our method effectively enables song reinterpretation while preserving vocal melodies, the inherent limitations of ACE-Step's text control capabilities (Gong et al.] 2025) restrict fine- grained control over vocal timbre, supporting only gender-based adjustments and limiting nuanced voice manipulation. This constraint limits the flexibility of cover song generation, which future advancements song generation foundation models may address. Alternttvely, we plan integrate speaker encoder in the future, such as those used in Singing Voice Conversion (SVC), to enable more nuanced and expressive cover song generation.
In this work, we exclude the song-specific, local adaptations (e.g., variations in phoneme durations, vibrato, and note transitions) that musicians may introduce when creating covers. One promising avenue toward Whitney Houston-style expressive covers is to combine our method with melody editing tools or human creators. Local creative modifications can be introduced via external editing or live performance, after which model generates global reinterpretation of the revised melody contour. Additionally, Al-generated songs lack the expressive subtlety of human singing and fine- grained vocal technique annotations, preventing our model from achieving the micro-level expres- siveness typical of professional covers. Future research could incorporate such fine-grained control by developing song generation models capable of understanding time-aligned musical prompts for precise adaptation control. More ideally, by constructing real paired original-cove datasets, mod- els could learn autonomously reinterpret an incomplete melody, employing both global and local adaptations to convey distinet emotions and styles.
CONCLUSION
We introduce lightweight framework for cover song generation built upon a text-to-song model. To achieve precise melodic control and harmonious integration, we propose a novel conditioning method, LA-EiLM, which enhances the conditional injection mechanism and conditional represen- tation. The EiLM facilitates temporally aligned modulation of the generative hidden states based on conditioning inputs, while the IACR module employs adaptive refinement, leveraging hidden states to enhance the integration of conditional features into the generative model. Experiments demon- strate that our approach outperforms state-of-the-arr selody-coottoollable music generation methods while requiring significantly fewer trainable parameters. IA-EiLM significantly improves the gener- ation quality and melody preservation for cover songs. Theoretically. IA- IA-EiLM shows potential for application in various conditional tasks beyond controllable music generation. The curated Suno70k dataset helps mitigate copyright issues in song-related Al tasks, supporting advancements in Al mu- sic research.
ETHICS STATEMENT
In exploring cover song generation, we have given full consideration to the ethics of copyright. To mitigate these issues, model was trained exclusively Al-generated music. All outputs strictly for non-commercial academic demonstration, and we are committed to the responsible use of this technology.
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REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our research, we provide detailed description of the dataset pro- cessing pipeline in Section Comprehensive details of computational resources, parameter settings, and evaluation protocols are included in Section 5 Additionally, we release the code, dataset, and audio samples at at /ttpt//gttbb//////// com/ 1s www.isuuifffffoooooo /
ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62572458, in part by the China Scholarship Council (CSC) under No. 202504910321, in part by the German Research Foundation (DFG) under Germany's Excellence Strategy EXC 2117 422037984, and in part by the National Science and Technology Council, Taiwan under Grant 113-2221-E006-111-MY3.
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A RELATED WORK
A.1 TEXT-TO-SONG GENERATION
Jukebox (Dhariwal et al. 2020 pioneered the generation of song, utilizing a multiscale Vector Quan- tized Variational Autoencoder (VQ-VAE) to compress raw audio into discrete codes, which are sub- sequently modeled using autoregressive Transformers. In recent years, several industry tools, such as Sund ). Seed-Music (Bai al. 2024)). Meruka have demonstrated promising results in song generation. This progress has spurred researchers to focus on developing open-source text-to- song models, which are increasingly competitive with their closed-soure counterparts.
Melodist (Hong et al. 2024) and Melody (Li et al. 2024a) employ two-stage generation process. sequentially producing vocals and accompaniment to create the final song. Songcreator (Lei al. 2024) integrates a dual-sequence language model with diffusion model to achieve lyrics-to-song generation. Meanwhile, YuE (Yuan et al. 2025)) and SongGen (Liu et al.)) explore the potential of separating vocal and accompaniment tokens in song generation. Building 2025) introduces mixed-token generation and leverages Direct Preference Optimization (DPO) to enhance the musicality and instruction- -ooluctton-followinng capabilities of generated songs. A notable limita- tion of autoregressive model-based approaches is their prolonged inference times. Diffusion-based methods, such as DiffRhythm (Ning et al. (025) and AceStep (Gong et al. (025), have signif- icantly mitigated this issue. AceStep (Gong et al. (025h) further addresses the shortcomings of DihlRhythm (Nig et al., 2025) by adding the understanding of song structure. Although current models generate high-quality songs and some support audio prompts, precise temporal melody con- trol remains challenging. To optimize inference speed and performance, we adopt AceStep as our base model.
this, Le (Lei al.
A.2 SING VOICE SYNTHESIS & CONVERSION
Singing Voice Synthesis (SVS) aims generate single-track vocals consistent with given lyrics and musical scores. Previous GAN-based approaches (Huang et al. 2022; Chunhui et al. 2023) suffer from over-smoothing and unstable training, respectively, compromising the naturalness of synthe- sized singing. Some methods (Zhang et al. (0226) 20236 Cui et al. 2024)) leverage the VITS text- to-speech framework for end-to-end SVS. Choi et al. (Choi & Nam 2022) propose an SVS method that only needs audio-and-lyrics pairs, eliminating the need for duration labels. DiffSinger (Liu jet al. ) pioneers the use of diffusion models in SVS, significantly enhancing voice quality. While MuSE-SVS (Kim et al. (203)) introduces singer and emotion control, TCsinger (Zhang et al.) 2024ah) and TCsinger2 (Zhang et al. 2025)) further explore zero-shot style-controllable SVS. Sing Voice Conversion (VC) seeks to transform the timbre and singing techniques of a source singer into those a target singer while preserving song content and melody. DifISVC (Liu et al.,
202T) ap-
plies diffusion models to SVC, improving generation quality, while CoMoSVC (Lu al. 2024 and
LCM-SVC (Chen et al.) 2024) focus on accelerating diffusion inference. Additionally. SO-VITS-
SVC 10 FreeSVC (Ferreira et al. 2025)), and KNN-SVC (Shao al. 2025)) achieve zero-shot SVC. While these works delve into melodic control, they remain limited to single-track, short- duration vocal synthesis. In contrast, our task targets the simultaneous generation of full-length accompaniment and vocals.
A.3 CONTROLLABLE MUSIC GENERATION
Controllable music generation enhances mux-io--msicc generation by integrating temporal trol. AirGen (Lin et al. 2024) employs parameter-efficient fine-tuning (PEFT) based on MUSIC- GEN (Copet al. 2024) for content-based music generation and editing. Li et al. (Li al. 2024b) adapt textual inversion (Gal et al. 2023)) into time-varying textual inversion with a bias-reduced stylization technique for example- style transfer. MusiConGen (Lan al. (024h) introduces an in-attention mechanism and efficient fine-tuning to control rhythm and chords. Music Control-
"htps://www.mrrkkaa hhtms///gthh..mmmm//ccdddee/e///
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Net (Wu et al. 2024) applies ControlNet (haag et al. 2023a to diffusion model for text-to- music generation, enabling precise temporal control. Ciranni et al. (Ciranni et al., (025 )) and Hou et al. (Hou et al., 2025 augment Stable Audio (Evans et al. 2024 ) a DiT-based model, with ControlNez-inspired control branch. However, their reliance on element-wise addition limits control flexibility due to its inherent simplicity. MuseControll.a Lite (Tsai et al. (025)). inspired by IP-adapter, designs lightweight adapter for controllable music generation. SongEditor (Yang et al. (025) uses cross-attention inject audio conditions, achieving complete vocal accompaniment tracks when given the rest. However, these cross-attention- based methods require the model to implicitly learn the temporal alignment between the condition and the music tokens. This approach is not only indirect but also incurs significant computational redundancy. Furthermore, existing methods lack adaptive modulation of conditions with original hidden states.
A.4 CONDITIONAL NORMALIZATION
Conditional Normalization methods leverage learned function of conditioning information to de- rive modulation parameters for affine transformations of target features, proving highly effective across various domains. Conditional Instance Normalization (Dumoulin et al. 2017)) and Adaptive Instance Normalization (AdaIN) (Huang & Belongie 2017) excel in image style transfer. Con- ditional Batch Normalization (Anderson al. 2018) supports general visual question answering. while Dynamic Layer Normalization (Kim et al. 2017) enhances speech recognition. (Perez et al. 2018 unify these methods with Feature-wise Linear Modulation (FiLM), SPADE (Prk et al. 2019)) injects semantic segmentation maps for image translation, and similarly, we propose EiLM for temporally adaptive gelodd-conditionen control. Temporal FiL.M (TFiLM) Birnbaum et al. (2019: [omunnià et al. (2023) applies FiLM sequentially within an RNN Elman (1990) frame- work to capture onngrangge dependencies, demonstrating robust performance
text classification and black-box audio effect modeling. Recent work DiT (Peebles & Xie 2023) incorporates condi- tional information in text-to-image models via the AdaLN- module. Despite these advances, the application of conditional normalization in music remains underexplored.
A.5 SONG DATASET
Some of the song datasets for Music Information Retrieval (MIR), such as MIR-1K (Hsu & Jang 2009]. MIR-ST500 (Wang & Jang, / and "mediz") include audio files with rich annota- tions but are limited to small scales, typically comprising only hundreds of samples. Large-scale song datasets, including WASABI Song Corpus (Buffa al. 202I]. Million Song Dataset (Bertin-l Mahicux et al.] 201T), and SongCompose-PT (Ding et al. 2024), primarily provide metadata and analytical features but lack raw audio. Datasets designed for singing voice synthesis, such as OpenSinger (Huang et al.) (021). M4singer (Zhang et al.] 2022a), and GTsinger (Zhang et al.) 2024b), consist of short, single-track vocal segments, making them incompatible with the require- ments of Cover Song Generation. Recently, SongEval (Yao et al., 2025) introduced benchmark dataset of 2,399 Al-generated full songs, but its inconsistent quality limits its applicability.
B METHOD DETAILS
B.1 NOTE-LYRICS ALIGNMENT
We condition on the RVMPE-extracted FO sequence, which is preprocessed by normalizing only its voiced components (50-900Hz) and concatenating the result with derived binary voiced/unvoiced flag (wv_flag) to form the final melody feature. Our method achieves lyric-to-note alignment with- out explicit duration modeling external aligners. The uv-flag accurately delineates voiced re- gions, and visualization (see Figure shows that phoneme transitions consistently align with inflee- tion points in the FO curve. By jointly optimizing melody (FO) and linguistic content (phonemes) during source song reconstruction, the model leverages the strong phoneme-non note dependencies cap- tured by its pretrained backbone to implicitly construct phoneme layout along the FO timeline.
Transcription from Polyphonic Music
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B.2
VALIDITY OF EQUATION REGARDING AUDIO COPYING
Given Eq. 6
(Tm, Pm) arg min [m h + Mell 7,9
(14)
where both and 3 are static (i.e., independent the hidden states h).
Well-constrained case (7m = 0). The objective simplifies to ming (l) Melle. which has unique solution for any
Underconstrained case (rm * 0). A static (%, () must satisfy Em () Mc simultaneously for all possible hidden states h. This is impossible unless degenerates to a constant vector.
Why 7m works for full-audio conditioning When the condition m is the full target audio (i.e., the modeling objective effectively becomes reconstructing via y O + 3 m), the optimal solution to Eq. 6 is
Ymm
Bm ~ m,
(15)
which completely suppresses the hidden state h and directly copies the condition. This is exactly what MuseControlL.it does, as confirmed by its diagonal attention pattern under full audio condi- tioning (see Fig. 2 in the MuseControlLite Tsai et al. (2025) appendix Fig. [ appendix). When the attention matrix is always diagonal, the query degenerates into a pure positional index and suppresses h. The output of the attention layer then becomes:
Qrke Vdl
Output Softmax
Ve = V Va:
(16)
where Qn denotes the query from hidden state h, and Ke, V. are derived from the audio condition m. This process directly duplicates V, rather than generating new content.
Why 7/m = O fails for melody control In our task, the condition m corresponds to compressed melody rather than the target latent. Setting Tm causes the modulated hidden states to contain only melody information. They lose essential attributes of the target song (e.g.. timbre and lyrics), making it impossible generate the target song. This establishes that 7mm * is necessary for task. However, when 7m * O, with and 3 fixed across all hidden states h, Eq. 6 becomes underconstrained for arbitrary h. Our proposed IACR strategy is introduced specifically to make the objective well-constrained again.
In summary, Audio copying succeeds with static conditioning only because it can exploit the de- generate Y' 0 solution-a shortcut not available for melody control. Our IACR resolves the undercoostrained problem by making (r, B) dependent on both m and h (Eq. 7), yielding obvious improvement when ablated (Table Row 2 Row 6).
C ADDITIONAL EXPERIMENTAL DETAILS
C.1 DETAILS BASELINES
ACE-Step+SA ControlNet (Hou et al. 2025; Gong et al. 2025). We follow the architecture of SA-ControlNet, while replacing its generative backbone with the pre-trained ACE-Step model. Specifically, we clone half of the pre-trained Diffusion Transformer (DiT) blocks to create control branch consisting 12 Transformer blocks in total, while keeping the melody encoder exactly the same in our proposed method. The model is trained with the same training settings as model (AdamW, =099,, 32=0.95, weight decay=0.01, Ir=10-4 1,000-step linear warm-up). Training lasts for 32,000 steps with batch size of 12 (vs. 30,000 steps for model). During inference, directly adopt the original classifier-fret guidance (CFG) sampler of ACE-Step with guidance scale 15.0.
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644
10/200
300
named
hair
werea't
Curve with Phoneme
500
406
0000
Figure We visualize the FO contour extracted from the song, along with the word-level and phoneme-level timestamps produced by the Montreal Forced Aligner (MFA) (McAuliffe et al. 2017]. The full lyrics used in the example are: "I had a horse named Hair, we weren't 't going nowhere."
Sample IF Iverse
[verse]
dowlt meed you no more dlo'tt want you Love we vo moore
In shadows where monsters play Echoes of our darkest day
No more
Say that you love mee Turn ground and baciesteb waz pain of forgiveness, eves time coulaleet this
But we won't fade to gray
well rise up come what way
Sample 2
[verse
[verss
Sold, r'l double this tax. and leiss my deed goodbbee
Through fire and lay tears deerpes feors The whispers turn to oheers The streugth is all we hear
Butt the dealer's get 21 she's
confreacing
stucole with flve now ske's bettiing the eays kope to stay allve marlene
warlene puct daww the slot.
(a) Sample lyrics transcribed the SongEval
(b) Sample lyrics from Suno70k
Figure 4: Lyrics transcribed by Whisper (Radford et al., 2023) with All-in-One (Yao et al. 2025) for SongEval (Yao al., 2025) exhibit punctuation errors, including run-on sentences (orange), multiple clauses per line (bluc), and incorrect sentence breaks (green), whereas Suno70k's 's native lyrics ensure each phrase on separate line.
ACT-SStp=-MuseeCottrllitt (T'a et al., 2025) Gong et al., 2025). We follow the architec- ture of MuseControlLite, while replacing generative backbone with the pre-trained ACB-Step model. Specifically, we adopt its decoupled cross-attention mechanism equipped with Rotary Posi- tion Embedding (RoPE), while keeping the melody encoder identical to that used in our proposed method. The model is trained with the same training settings as our model (AdamW, 8 =0.9,, $2=0.95, weight decay=0.01, 10-10-4 1,000-step linear warm-up). Training performed for 36,000 steps with batch size of 12 (vs. 30,000 steps for our model). During inference, we evaluate three guidance strategies: (1) the original ACE-Step classifier-free guidance (CFG) with guidance scale 15.0: (2) MuseControlLite-sstyy Multiple Classifier-Free Guidance with =1.0 and Amelody -7.; (3) Multiple Classifier- Free Guidance with 7 and Amelody=15.00 The first strat- egy (ACE-Step's native CFG, >=15.0) yields the best overall performance. Results for the three variants are summarized in Table The first version achieves the best overall performance and reported in main text.
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Table 6: Quantitative evaluation results of three inference guidance strategies for the ACE-
Step+MuseControlLic baseline (Tsai et al. 2025).
RPA 1
RCA 1
OA
CLAP
FD 1
KL.
PER
A 15.0
0.5205
0.5346
0.5940
0.2977
72.04
0.2151
0.4194
Sunn70k
Accot
15.0, Avoolody 7.0
0.4896
0.5042
0.5557
0,3159
55.82
0.1820
0.4837
Acezi 7.0, Amelodd
15.0
0.5536
0,5653
0.6351
0.2942
73.59
0.2763
0.6159
= 15.0
0,5164
0.5275
0.6025
0,2462
68.73
0.2764
0.4758
wSappe Tags
Accot 15.0, Avaetady 7.0
0.4798
0.4924
0.5669
0.2704
54.11
0.3087
0.5446
Acezt 7.0, Amelldy = 15.0
0.5578
0,5682
0.6459
0.2290
74.39
0,3445
0.6387
A 15.0
0,5421
0.5498
0.6208
0. 0,2600
90.19
0.3913
0.5760
SongEval
Acas 15.0. Aunelady 7.0
0,5040
0.5115
0,5800
0.2699
77.36
0.3205
0.6333
Auez 7.0, Amelldd = 15.0
0,5880
0,5954
0.6614
0.2567
97.
0.4593
0.7179
Table 7: Comparison SvngEval Yaoo et al. 2025 Aesthetics Metrics Across Methods.
Dataset
Suno70k
SongEval
Mosiis
Caleresuee
Musicality
Munncabiltty
Clarity
Natardaess
Colacienne
Masicality
Menuesbilty
Clarky 1
Naturalnoa
ACE-Step
Songticho (Ulon)
C.2 COMPARISON OF LYRICS FROM SONGEVAL AND SUNO70K
Lyries transcribed by Whisper (Radford et al. 2023) with All-in-One Yao et 2025 for SongEval (Yao et al. 2025) exhibit punctuation errors, including run-on sentences, multiple clauses per line, and incorrect sentence breaks, due to inaccurate segment splitting by Whisper. In contrast, Suno70k's native lyrics ensure each phrase occupies separate line (See Fig. 4 During training, this implicitly fosters alignment between lyries and melody, with each melodic phrase correspond- ing to one lyric line. However, transcribed lyrics disrupt this alignment, leading to increased PER in the SongEval dataset evaluation. The lesser impact on the original model stems from its lack of melody control, it generates lyrics sequentially without requiring lyric-melody alignment, thus relying less on accurate sentence segmentation.
C.3 AESTHETIC EVALUATION
We perform an aesthetic evaluation of the results from two datasets using SongEval (Yao et al., 2025). shown in Table 7 Each song is evaluated across five dimensions: overall coherence, memorability, naturalness musicality. Our method exhibits clear advantage over other approaches across all metrics, further validating the harmonious integration of melody control and the generative model, thereby generat- ing high-aesthetic cover songs.
vocal breathing and phrasing, clarity of song structure, and overall
D THE USE OF LARGE LANGUAGE MODELS (LLMs)
We used large language models (LLMs) general-purpose writing assistant to polish the text of this paper. Specifically, LLMs were employed to correct grammar, improve clarity, and refine phrasing in certain sentences. The models did not contribute to the research design, problem formu- lation, method development, experimentation, interpretation of results, or overall scientific contri- butions. Their role was limited solely to surface-level editing and presentation improvements of the manuscript.
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Figure 5: Attention map visualization of MuseControlLite Taa et al. 2025) under full-audio con- ditioning. The clear diagonal pattern indicates that the post-softmax attention matrix approximates an identity matrix.
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Reviews:
[{"review_id": "CaMf984RhW", "reviewer": "Reviewer_PTPX", "summary": "This paper investigates the interesting task of cover song generation and proposes a parameter-efficient framework based on ACE-Step to enable melody-controlled song generation. It introduces EiLM, an efficient condition injection mechanism, and IACR, which dynamically adapts conditioning to the model’s hidden states. The authors also release the Suno-70k dataset. The paper is clearly written, and the experiments are thorough and convincing.", "questions": "See Weakness section above.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper formalizes an intersting task—melody-controlled cover song generation—and carefully discusses the limitations of existing condition-injection mechanisms in NAR frameworks. The proposed IA-EiLM achieves superior performance in parameter efficiency, precise temporal control, and melody adherence.\n2. The experiments and ablation studies are comprehensive and well-designed.\n3. The Suno-70k dataset represents a meaningful contribution to the research community.\n4. The presentation is clear, with well-designed figures and an easy-to-follow narrative.", "weaknesses": "1. The paper lacks details on the exact form of melody input. If it only uses pitch sequences, how is the alignment between notes and lyrics ensured? Why was this particular representation chosen, and were other melody representations considered?\n2.  How does the model handle conflicts between text tags and melody? Since a melody implicitly encodes stylistic attributes, it would be useful to clarify how such inconsistencies are resolved.\n3.  Although SongEval Aesthetics Metrics are included in the appendix, it remains unclear whether adding melody control compromises ACE-Step’s original generation quality. A demo comparison with ACE-Step would be helpful.\n4.  Minor: In the section “Why is IACR necessary?”, the symbols E, T, and M are undefined, which makes the discussion slightly confusing.", "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": "zYZlFe6NHX", "reviewer": "Reviewer_ACB8", "summary": "The paper employs an Instance-Adaptive Element-wise Linear Modulation (IA-EiLM) framework to enable controllable song generation. Specifically, Element-wise Linear Modulation (EiLM) improves the temporal alignment of melody control, while Instance-Adaptive Condition Refinement (IACR) enhances the condition representation. To address the scarcity of large-scale, open-source full-song datasets, the authors construct a large-scale dataset.", "questions": "Can the technique proposed in this paper be extended to autoregressive singing voice synthesis?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper proposes Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), which comprises the EiLM and Instance-Adaptive Condition Refinement (IACR), enhancing the condition injection mechanism and conditional representation, respectively.\nThis paper introduces Suno70k, an open-source AI song dataset enriched with detailed annotations, including enhanced tags and lyrics.", "weaknesses": "One of the paper's claimed innovations, EiLM, appears to be a relatively trivial extension of FiLM. This leaves IACR as the paper's primary technical insight, which may render the overall technical novelty somewhat limited.", "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": "TJuLFBDPoZ", "reviewer": "Reviewer_5LhQ", "summary": "The paper presents a novel approach for what is described as cover song generation, but should better be denoted as music generation with melody conditioning. The model takes as input the lyrics, a reference melody, and other control features, and generates both singing voice and accompaniment audio. It is built by means of adapting the foundational model ACE-Step. Instance-Adaptive Element-wise Linear Modulation (IA-EiLM) for temporal conditioning, together with Instance-Adaptive Condition Refinement (IACR) to inject conditioning information during diffusion. The proposed model enables coordinated generation of vocals and accompaniment in a stylistically consistent and coherent manner. To support further research in this area, the authors propose a new dataset of synthetic music collected from Suno, denoted Suno70k.", "questions": "I feel the paper is a significant contribution with interesting results. The main weakness is related to incorrect statements and misleading presentation. Despite these positive findings, in my opinion and in its current form, the paper has to be rejected due to the fact that it sells existing technologies as new contributions.  I suggest the following modifications:\n\n- Correctly cite previous approaches that are equivalent to your *EiLM* and *IACR* methods. These are not new and should be correctly introduced as *Temporal FiLM* or *Time-varying FiLM* and *multiplicative gating*. The applicative context is certainly new, and they can therefore be presented as new contributions. If I did get this wrong and there are fundamental differences that warrant a new term, please explain the differences.\n\n- contextualize the example in the introduction and elaborate on the way your method might be used to achieve what Withney Houston did in terms of reinterpretation. I think that you would basically need to get a new melody contour from a singing performance, whistling, or this could even be a performance with another instrument (a saxophone could be interesting). The model would then have to generate suitable phoneme durations, intensity contours, and voice qualities. I think it would be helpful to discuss the limitations of using synthetic datasets to create such reinterpretations. \n\nIn the context of the present article, I feel that the following statement: *We believe that endowing machines with this artistically creative process of reinterpretation represents a promising avenue for empowering music through artificial intelligence.* is unrelated to the proposed method. I would say that if you want to create a model that can reinterpret a song such that it transforms  *a gentle country ballad into a worldwide anthem of deep affection* and if you want the model to learn doing this by itself,  it would be necessary to keep the melody controls incomplete, for example, midi, and rely on the textual descriptions to control how the melody is interpreted. This, however, is not the topic of the present paper.\n\n- Correct or properly contextualize the statement concerning controllable generation and control injection.\n\n- Correct the presentation of the alternative methods in the tables to avoid the misleading impression that you compare to these methods as a whole and not to adaptations of your model that follow the ideas of the references.\n\n- Please reformulate all references to *optimal performance*. Either present a proof that your solution is indeed optimal, or use another term like *the best performance of all methods evaluated*, *superior* compared to the baseline methods.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- adaptation of a pretrained linear DiT model to a new control, establishing a new approach for melody conditioning of music generation\n- two rebranded approaches EiLM and ICAR for conditioning\n- a new large dataset of full music, including singing voice and lyrics annotations.\n- successful evaluation of the proposed methods.", "weaknesses": "The style of presentation is rather unclear and confusing:\n \n- in the abstract the authors introduce the term cover song generation as: (Line 17) *We formalize this challenge as Cover Song Generation, which requires preserving the source vocal melody while simultaneously synthesizing new vocals and accompaniment, posing higher demands for controllable music generation.* they later change into:  (line 40) *...reinterpret the original’s emotional and stylistic core, evolving a gentle country ballad into a worldwide anthem of deep affection. Such reinterpretations amplify a song’s cultural impact and illustrate the creative potential of musical reimagination.*\n  \nI would stress that the Whitney Houston example demonstrates exactly what the proposed model cannot achieve. Whitney Houston alters phoneme durations, vibrato, intensity, and note transitions to modify the emotional expressivity. Conditioning the model on a fixed melodic contour from the original song is incompatible with this goal. There are clearly ways to use melody conditioning to achieve some sort of reinterpretation. It would be helpful to briefly describe how this could be achieved. (See questions.)\n\n___\n\n- Line 093: *Feature-wise Linear Modulation (FiLM) (Perez et al., 2018) has demonstrated efficacy as a conditioning technique. However, FiLM is limited to injecting global conditions, uniformly modulating all tokens within an instance, making it unsuitable for time-varying features like melodies.*\n \nIt is unclear to me how this claim can be made. While it is true that FiLM is most of the time used for global features, there is nothing in the technique that prevents one from using it to represent time-varying features.  I'd refer to the authors to   \n  1. Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulation. (https://proceedings.neurips.cc/paper/2019/file/2afc4dfb14e55c6face649a1d0c1025b-Paper.pdf)\n  2. MODELLING BLACK-BOX AUDIO EFFECTS WITH TIME-VARYING FEATURE MODULATION\n(https://arxiv.org/pdf/2211.00497)\nWhile I agree that these papers do not apply the temporal FiLM to music generation, the statement as given is nevertheless clearly wrong. \n\n___\n- (Line 190) *...that rely on the cross-attention mechanism or element-wise addition, we introduce a more flexible injection mechanism to incorporate melody control into generative models.* \n\nordering the three methods: cross attention (CA), element-wise addition (EA), and temporal FiLM (TF), according to flexibility, I would come to CA is most flexible, TF is second in the list, EA is last. So this statement looks confusing. \n\n___\n- (Line 209) *In addition to external improvements to the condition injection mechanism, we introduce an internal method for improving conditional representations, termed Instance-Adaptive Condition Refinement (IACR).* \n\nFor me the proposed IACR is a multiplicative gating which was introduced in the WaveNet paper in 2016. The application context is different, but the principle is the same. \n\n___\n\n- Line 225 and following: *To the best of our knowledge, existing controllable generation methods derive conditional features solely from the conditional input, overlooking their compatibility with the generative model’s hidden state.* \n\nIt appears to me that in its generality, the statement is incorrect.  The problem you are trying to solve is not controllable generation but control injection, which is adapting an existing pretrained generator with new controls. \n\n___\n- Line 346: *We compare our method... As both methods support only instrumental music generation, we apply them to the same base model, ACE-Step, used in our approach, and ensure consistency in the melody encoder.* \n \nMy problem here is that in all tables, you present the methods using only the reference to the original papers as if you have used these methods unchanged. This is misleading. It appears necessary to present this as ACE-Step + SA Controlent, ACE-Step + MuseControLite. \n\n- Line 360: *Our approach achieves the optimal performance across all metrics.* and line 425 * while achieving optimal performance in controlling vocal melodies.* \n\nThe term *optimal* implies that the solution has been proven to reach the best possible solution of all possible configurations. I do not see such proof.", "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": "0K0bUioIiI", "reviewer": "Reviewer_dAnD", "summary": "The paper formalizes Cover Song Generation, preserving the vocal melody while generating a new song. The authors compare three methods for controllable music (song) generation: ControlNet, MuseControlLite, and their proposed model, SongEcho. Through multiple experiments, they show that SongEcho outperforms the others in terms of music quality, text adherence, and vocal melody preservation.", "questions": "1.  Can the authors provide more details about the training and inference specifics?\n2.  I understand that the formulation of 'Cover Song Generation' limits the definition to 'using the same vocal melody and regenerating it,' which might be easier. However, a cover song traditionally implies that the musical elements, such as instrumentation, tempo, or melodic phrasing, might change, while the fundamental character of the song remains the same. How will you address your work in light of this definition? Otherwise, the work might be more closely related to a vocal-to-accompaniment generation task.\n3. The authors claim that \"cross-attention mechanism\" are redundant. However, in the MuseControlLite paper, they claim that this is for the use of improvisation (i.e. if only partial condition is provided, the model can improvise for the rest), does songecho support this?\n\nThis is an interesting paper, I am willing to raise the score if answered properly.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. By using fewer parameters to adapt Acestep to the Cover Song Generation task, it demonstrates better performance compared to other fine-tuning methods, showing the effectiveness of the proposed approach.\n2. Provides a critical analysis across different conditioning mechanisms.\n3. The authors promised to open-source a dataset enriched with detailed annotations, including enhanced tags and lyrics.", "weaknesses": "1. The authors adopt two baseline methods that are not originally applied to Acestep. The authors should provide implementation details for these baseline methods (e.g., training specifics, inference specifics) to ensure that both the baseline methods and the proposed method are treated equally.\n2. The original baseline methods might not be suitable for Acestep. ControlNet can be applied by simply copying parts of Acestep; however, regarding the linear attention architecture, MuseControlLite might require modifications since it uses the traditional cross-attention method with positional encoding and zero-convolution. There might be a misalignment if the cross-attention layers in Acestep use linear attention, but the decoupled cross-attention in MuseControlLite are not. Additionally, the original MuseControlLite paper indicates that during inference, multiple classifier-free guidance is necessary. The authors should provide details for both the training and inference processes for the baseline methods.\n3. The authors indicate that the ControlNet method is not fully trainable, but only the LoRA adapters are trainable. This makes the comparison less persuasive; perhaps the authors should try mixed-precision training.\n4. The authors define 'Cover Song Generation' as 'preserving the source vocal melody while simultaneously synthesizing new vocals and accompaniment'; however, generating a song with the exact same vocal melody might not fully represent 'cover song' generation. A 'cover song' does not mean that the vocal melody has to be exactly the same.", "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)