SONICS: Synthetic Or Not - Identifying Counterfeit Songs

Authors: Md Awsafur Rahman, Zaber Ibn Abdul Hakim, Najibul Haque Sarker, Bishmoy Paul, Shaikh Anowarul Fattah
Primary area: datasets and benchmarks
Keywords: deepfake detection, fake song detection, synthetic song detection, efficient model, dataset, audio processing

Abstract:
The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. For long songs, our top-performing variant outperforms ViT by 8% in F1 score, is 38% faster, and uses 26% less memory, while also surpassing ConvNeXt with a 1% F1 score gain, 20% speed boost, and 67% memory reduction.

Decision: accept (poster)
Reviewer scores: 6, 5, 6, 3

Reviews and discussion:
<thread>
creation date:
1730140145832

thread_type:
official_review

<thread_object>
creator: reviewer_zntb

<content>
summary:
This paper introduces SONICS, a large-scale dataset designed for end-to-end synthetic song detection, addressing the limitations of existing datasets such as lack of musical diversity, short song durations, and absence of fully AI-generated music. They also propose a novel model, SpecTTTra, which excels in capturing long-range temporal dependencies within songs, outperforming current models in terms of speed and memory efficiency. The paper establishes both human and AI benchmarks to advance research in distinguishing AI-generated music, thereby protecting artistic integrity.

strengths:
The paper presents three data acquisition pipelines for fake songs, significantly expanding the dataset for synthetic song detection and providing valuable reference for peers looking to augment this dataset. The paper also introduces a new model for synthetic song detection that achieves better detection results while reducing training costs, offering a new reference method for the field.

weaknesses:
1. The data collection for fake songs is not comprehensive, leading to potential biases. Only songs generated by Suno and Udio were collected, while other AI music generation tools such as SeedMusic and SkyMusic exist in the market.
2. Music generated by AI tools from the same company tends to have a consistent style. The authors need to demonstrate how they can distinguish between real and fake songs, rather than just identifying the music styles of Suno and Udio. Suno and Udio's song styles are concentrated in certain genres that, while present in real-world music, are less common. For instance, if the model achieves an 80% accuracy rate, it might mean that 20% of the ground truth song collection consists of pop and rock styles generated by Suno and Udio, while the remaining 80% are styles rarely generated by these tools. Thus, the model might be performing genre classification rather than accurately distinguishing between real and fake songs.
   I suggest that to reduce this interference and truly identify AI and non-AI differences, rather than stylistic differences, the authors could introduce songs generated by other AI tools, different from the training data, as a test set. For example, if the model is trained to distinguish between real and fake songs using data from Suno and Udio, it could be tested with songs generated by SkyMusic, SeedMusic, and other models. If the model can still identify songs from SkyMusic and SeedMusic as fake, it would demonstrate the model's effectiveness.

3. The authors need to prove that they are identifying real songs versus fake songs, not just differences in audio quality. The dataset includes real songs and AI-generated songs. Real songs are sourced from YouTube, where the sampling rate is generally higher and the audio quality is better. In contrast, AI-generated songs mentioned in the paper have a sampling rate of 16kHz/32kHz, which is of poorer quality, and even if upscaled to match YouTube's quality, there would be significant loss in the high-frequency spectrum.
   The paper does not explore or explain this aspect, nor does it mention whether the audio quality was normalized before training and testing. It is unclear whether the model has truly learned to distinguish AI-generated songs or if it has been judging audio quality.

   Whether it's distinguishing between 16kHz resolution songs and high-resolution songs or differentiating between up-sampled 16kHz songs and original high-resolution songs, it's clear that these are audio quality tasks, not real song vs fake song recognition tasks. Only by reducing all to the same low-quality audio can the bias of audio quality on the model be avoided.

4. The experimental tables in the paper do not indicate which metrics are the best, making it difficult to intuitively compare the effectiveness from the table.
5. There is a lack of Ablation Study to show the model's performance using only Spectral or Temporal information.
6. There is no experimental comparison with other models and methods used in Synthetic Song Detection papers.

questions:
1. The dataset's limited scope to Suno and Udio-generated songs may introduce bias, overlooking other AI music tools like SeedMusic and SkyMusic.
2. The model's high accuracy might reflect genre classification rather than distinguishing real from fake songs due to the focused style of Suno and Udio.
3. The study lacks evidence to confirm the model's ability to identify fake songs independently of audio quality differences.
4. The paper's experimental tables fail to highlight the best-performing metrics, hindering clear comparison.
5. An Ablation Study is missing to assess the model's reliance on Spectral or Temporal information alone.
6. The paper does not compare the model's performance with other methods from the Synthetic Song Detection literature.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730151074591

thread_type:
official_review

<thread_object>
creator: reviewer_ladp

<content>
summary:
The paper introduces SONICS (Synthetic Or Not - Identifying Counterfeit Songs), featuring the largest synthetic song dataset to date with over 97,000 songs (4,751 hours), including 49,074 AI-generated songs from Suno and Udio platforms. SONICS includes end-to-end generated content with both music and lyrics, and features longer duration songs (averaging 176 seconds). To effectively detect these synthetic songs, the authors propose SpecTTTra (Spectro-Temporal Tokens Transformer), a novel architecture specifically designed for capturing long-range patterns in music. This new model demonstrates superior efficiency compared to traditional approaches, running 38% faster than ViT while using 26% less memory and achieving better detection performance, particularly with longer audio samples. This work provides essential tools for detecting AI-generated music as these technologies become increasingly sophisticated and widespread.

strengths:
- The paper presents a clear and reproducible workflow for synthetic song generation, with detailed documentation of the prompt engineering process, generation parameters, and data processing steps. 
- The generated song dataset can be an important addition to SVS and SVDD community.
- The Spectro-Temporal tokenizer uses a simple but seemingly effective way to capture the long-range information.

weaknesses:
- The paper lacks sample demonstrations of the generated synthetic songs for evaluation. Without access to audio examples, it is difficult for reviewers and readers to assess the quality and diversity of the generated content, as well as validate the claims about different types of synthetic artifacts (e.g., "Karaoke effect"). The authors should provide a representative set of samples across different generation categories (Full Fake, Half Fake, Mostly Fake) and generation platforms (Suno, Udio).
- The naming "SpecTTTra" (Spectro-Temporal Tokens Transformer) is potentially misleading as the architecture only employs a transformer encoder component rather than a full transformer architecture. Furthermore, while the model claims to better capture long-range dependencies, the paper lacks ablation studies to demonstrate this capability. The authors should provide specific experiments showing how the separate temporal and spectral tokenization contributes to capturing long-range patterns in music.
- The performance improvements over baseline models, particularly ConvNeXt, are modest given similar computational costs. The F1 score gain of 1% with 20% speed improvement doesn't strongly justify the introduction of a new architecture. The authors should either provide additional experiments demonstrating clear advantages of SpecTTTra (e.g., ablation studies, analysis of captured patterns), or consider separating the dataset contribution from the model architecture into two papers.
- The quotation mark is used incorrectly.

questions:
- Could the authors provide a representative set of audio samples from different categories (Full Fake, Half Fake, Mostly Fake) and platforms (Suno, Udio)?
- What quality control measures were used to ensure the generated songs are suitable for the dataset?
- Could the authors provide ablation studies demonstrating how the separate temporal and spectral tokenization contributes to capturing long-range patterns?
- What was the rationale behind the specific clip sizes chosen for the three variants (α, β, γ)? And how sensitive is the model performance to these hyperparameters?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730632359537

thread_type:
official_review

<thread_object>
creator: reviewer_m36c

<content>
summary:
This paper introduces a dataset of real and "fake" music recordings, where the fake recordings have been generated using commercial music generation systems. An attempt has been made to match some characteristics between the real and fake recordings. In addition, a new transformer architecture is proposed that is aimed at capturing longer-term temporal context in music audio classification. Finally, results on the fake vs. real music classification task are presented.

strengths:
The introduction of a dataset for fake vs. real music classification is a welcome and very timely contribution. Different systems by two major commercial providers are considered and the dataset is separated in terms of different levels of fake (fully, mostly, half), which provides interesting insights - notably the half-fake songs with real lyrics seem to be easier to recognize. In the provided transformer architecture, I believe it makes a lot of sense to have one token per time step rather than several tokens for different frequency patches.

weaknesses:
Unfortunately, the steps of the dataset preparation pipeline do not seem to be evaluated rigorously, which limits the usefulness of the resulting dataset. I am also not very convinced by the usefulness of the proposed spectral tokens.
Overall, because of these and some other issues (see "Questions"), I recommend to reject this paper, although this is a borderline recommendation.

questions:
Major questions/comments:
- Line 81f: "the fake songs in our dataset are free from copyright issues, as they are generated through paid subscriptions that provide a license to use and share the content legally" --- Given that the generation models used are very likely to have been trained on copyrighted data, how can you ensure that the fake songs in your dataset are free from copyright issues? What if generations are identical or similar to existing real music recordings - in particular, when real lyrics and styles are used for generation (cf. "half fake" subset).
- Several steps in the dataset preparation pipeline will probably produce very noisy or incorrect results, but their performance is never evaluated. This is concerning, given that the dataset is the main contribution of this paper. In particular:
  - How well does PyAnnotate work for vocal detection on this data (line 189)?
  - How accurate are the extracted song-styles? This, in particular, I would expect to work rather poorly. Vocal type classification from music audio, for example, is a hard problem.
  - How accurate are the transcribed lyrics?
  - In the cases were generated instead of transcribed lyrics were used: How often do these regurgitate the lyrics from real songs?
- In the proposed SpecTTTra model, I am unconvinced that the proposed spectral clips can provide very helpful information for long songs. The resulting spectral tokens would contain something similar to an average spectrum, which would not be very informative for real vs. fake classification. Indeed, increasing the number $f$ of spectral clips does not improve results in Table 4. However, this was not independently evaluated from the choice of $t$.
- Following up on the previous comment, it seems like results for the SpecTTTra-$\alpha$ model are best, even though this utilizes the proposed architecture the least (low $f$ and $t$). 

Minor questions/comments:
- In Table 1, consider adding "Total hours".
- Line 142f: "Meanwhile, long audio classification remains an uncharted area in audio research." --- This statement seems too strong and should be removed. A wide range of music audio classification tasks involve long audio classification, for example: genre classification, global key detection, global tempo estimation, etc.
- Line 211f: How was the list of topics for the full fake generation obtained? The list given in the appendix seems very biased. Is "matt damon vs jimmy kimmel" really a representative example of a topic for a real song?
- Line 288: Why were significantly more MF songs than HF or FF songs included?
- Line 471: What was the musical experience level of the human annotators? Did they have access to spectral analysis tools (which could be helpful for the fake vs. real classification task)?

Additional comments:
- Line 12: "these inventions democratize music creation" --- This is a highly questionable, opinionated slogan that has its place in marketing but not in science. It should be removed.
- Line 68 (and others): Please do not use the bibliography to refer to companies such as Suno or Udio. A company is not a scientific publication. Same, e.g., for wandb (line 392).
- Table 4: Consider clarifying in the caption what the positive and negative classes are (i.e. what is real and what is fake). Furthermore, indicate which algorithms are seen and unseen during training.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730657381332

thread_type:
official_review

<thread_object>
creator: reviewer_heps

<content>
summary:
This paper presented a dataset for detecting AI generated music from well-known AI generated music services, Suno and Udio. The authors basically configured two group of music which are fake and real songs. For real songs, they retrieved 48,090 real songs from YouTube and for fake songs, they generated music from Suno and Udio. Finally, they trained a model for detecting whether the song is real or fake. To do this, SpecTTTra model is proposed. In this model, the authors proposed to use spectral and temporal patches (full-size in one dimension) to construct spectral and temporal tokens for their transformer-based classification model. The results showed that the proposed model is highly effective in detection. Also, the authors compared the model performance with Human performance.

strengths:
Overall, the paper is well-written and easy-to-read, and the details of the proposed method, experimental design, the dataset, and the experimental results are well addressed. I think the following researchers will be easily follow and use the proposed dataset, and the models as the authors described them well.

weaknesses:
It is good to see some research paper in the direction of detecting AI generated content in the domain of music. However, I think the paper lacks of analysis part. The scores that's shown in result tables seem to prove the effectiveness of the proposed method, however, without such in-depth analysis, the proposed dataset and the proposed method might not be useful in the future if the algorithms are updated. Therefore, I think having some analysis section would be beneficial for further future researches. (I think leaving some comments that is related to more high-level explanation on why the proposed model is detecting the AI generated content well, e.g. vocal characteristics, temporal/timbral characteristics, etc) At least, some additional case study on Appendix would be nice. For example, If some real song is detected as a fake, then try to give some analysis on why it's been detected as fake or vice versa.

questions:
I think the title of the paper and the term real and fake are a bit too generalized terms. 
For example, the proposed dataset is about detection on Suno and Udio generated contents. (not counterfeit songs, maybe synthetic is okay) 
I think the authors can carefully suggest a better title.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732950482536

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Official Comment on Generalization (Part 2 of 2)

comment:
## Generalization Test Results

| **Len. (sec)** | **Model**         	| **Seed Music**      	| **Sky Music**       	|
|----------------|-----------------------|-------------------------|-------------------------|
| 5          	| AASIST           	| 0.65                	| 0.56                	|
|            	| ResNet + Spec.   	| 0.52                	| 0.48                	|
|            	| ResNet + LFCC    	| 0.54                	| 0.51                	|
|            	| Wav2Vec2 + AASIST	| 0.69                	| 0.53                	|
|            	| ConvNeXt         	| 0.28                	| 0.21                	|
|            	| ViT              	| 0.68                	| 0.57                	|
|            	| EfficientViT     	| 0.67                	| 0.54                	|
|            	| SpecTTTra-γ      	| 0.51                	| 0.53                	|
|            	| SpecTTTra-β      	| 0.53                	| 0.56                	|
|            	| SpecTTTra-α      	| 0.55                	| 0.57                	|
| 120        	| AASIST           	| -                   	| -                   	|
|            	| ResNet + Spec.   	| 0.53                	| 0.35                	|
|            	| ResNet + LFCC    	| 0.56                	| 0.40                	|
|            	| Wav2Vec2 + AASIST	| -                   	| -                   	|
|            	| ConvNeXt         	| 0.36                	| 0.19                	|
|            	| ViT              	| 0.64                	| 0.53                	|
|            	| EfficientViT     	| 0.71                	| 0.55                	|
|            	| SpecTTTra-γ      	| **_0.80_**          	| **_0.60_**          	|
|            	| SpecTTTra-β      	| 0.53                	| 0.56                	|
|            	| SpecTTTra-α      	| 0.56                	| 0.54                	|
</content>
</thread_object>

</thread>
<thread>
creation date:
1732950522472

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Official Comment on Generalization (Part 1 of 2)

comment:
We appreciate the reviewers' concerns regarding generalization and aim to address them in this section. However, we would first like to reiterate that the **primary objective of this study was to introduce the first dataset specifically designed for detecting synthetic songs generated in an end-to-end manner**. A secondary objective was to emphasize the critical role of long-range context in synthetic song detection. In doing so, we also highlighted the computational challenges posed by existing methods, which motivated us to propose an efficient alternative with improved performance. Finally, we provided a human benchmark that has been absent in existing fake song detection literature.

Although generalization is not the primary focus of our research, we recognize its importance for understanding the potential of our proposed dataset and method. To evaluate this, we tested all methods trained on our SONICS dataset on songs generated by **SkyMusic** and **SeedMusic**, platforms distinct from those used for dataset creation. Due to the lack of an API for SkyMusic and the absence of a platform for SeedMusic, we were only able to manually collect 394 songs from SkyMusic and 36 from SeedMusic. While this sample size is limited, it provides a preliminary indication of the dataset’s and models’ generalization capabilities.

## Insights from Generalization Tests
The results, summarized in the `table in Part 2 of this discussion`, reveal several key findings:

- **Performance Shift**: Generalization remains a significant challenge, consistent with trends observed in media forensics literature [1, 2, 3]. Specifically, ConvNeXt, which excelled on the primary dataset, struggled significantly on external songs, dropping to the lowest rank across both durations. This underscores the difficulty of adapting baseline CNN models to unseen data sources.

- **SpecTTTra’s Robustness**: Our proposed model, SpecTTTra-$\gamma$, achieved the best F1 scores—**80%** for SeedMusic and **60%** for SkyMusic. However, larger variants of SpecTTTra (e.g., $\beta$ and $\alpha$) showed greater susceptibility to overfitting, though they still outperformed ConvNeXt in generalization tests.

- **Transformer Superiority**: Models incorporating transformers (e.g., SpecTTTra, ViT, EfficientViT) demonstrated greater resilience compared to CNN-based methods (ConvNeXt, ResNet), indicating their potential in addressing the broader generalization challenge.


## Implications and Future Directions  
The score drop observed when detecting synthetic data from out-of-distribution (OOD) generators aligns with patterns documented in prior literature [1, 2, 3], which often require sophisticated generalization strategies beyond the scope of our research. Nevertheless, **the ability of some models to achieve promising results on OOD generators without any sophisticated generalization strategies (e.g., [1], [2], [3]) highlights the potential of our dataset to address generalization challenges across platforms beyond Suno and Udio**, providing a foundation for future research on generalization in song forensics. It is worth noting that, to ensure the reliability of our results, we also tested all methods with randomly initialized weights (i.e., without training). None of the models achieved a score above 0.51.  

### Reference
- [1] Utkarsh Ojha, Yuheng Li, Yong Jae Lee, Towards Universal Fake Image Detectors that Generalize Across Generative Models. In CVPR 2023
- [2] David C. Epstein, Ishan Jain, Oliver Wang, Richard Zhang, Online Detection of AI-Generated Images. In ICCV 2023
- [3] Zhiyuan Yan, Yuhao Luo, Siwei Lyu, Qingshan Liu, Baoyuan Wu, Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection. In CVPR 2024
</content>
</thread_object>

</thread>
<thread>
creation date:
1733297441794

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Final Overview (Part 2 of 2)

comment:
### Responses
* **Using two sources (Suno and Udio) – Potential Bias**: Our dataset utilizes only Suno and Udio sources (covering five generative models) because these were the only accessible tools for end-to-end fake song generation at the time of dataset creation. Newer tools, such as SeedMusic and SkyMusic, were introduced later and also have technical constraints, such as the lack of APIs and platforms unlike Suno and Udio. Moreover, given the dynamic nature of the field, where keep updating the dataset is infeasible due to the constant emergence of new models, our dataset captures the state of the field during the defined collection period.

* **Generalization**: We referenced prior studies to highlight that generators leave specific artifacts in synthetic samples. Even with an increased number of generators, models are likely to focus on these generator-specific artifacts. Addressing this issue requires advanced method, as noted in the literature, which is a separate research domain, beyond our scope. Nonetheless, we tested the generalizability of all methods using out-of-distribution (OOD) generators (SkyMusic and SeedMusic) and observed the expected performance drop. However, despite the absence of advanced generalization techniques, some methods achieved promising performance. Specifically, SpecTTTra outperformed other methods, demonstrating the dataset’s potential to extend beyond Suno and Udio and fostering further research in generalization for song forensics. Note: Due to lack of API and Platform, we were only able to collect few samples from publicly available demo songs on their websites.

* **Efficiency and Performance**: The introduction of SpecTTTra is highly relevant to our dataset as we provide long audio samples and existing approaches become computationally heavy while processing them. Specifically, compared to ConvNeXt (best performing amongst previous approaches), we achieved 1% improvement on the F1 Score, while being 20% faster and using 67% less memory.

* **Existing Works**: Given the novelty of end-to-end fake song detection, there is no existing dataset thus no direct method for this task. However, we compared our work with the closest related methods, Singing Voice Deepfake Detection (SVDD), and our analysis shows that SpecTTTra outperforms these models in both performance and efficiency.

* **Contribution of Spectral and Temporal Tokens**: We conducted an ablation study (`Section 4.4 and Table 7`) that demonstrates both spectral and temporal information contribute meaningfully for fake song detection. While each can independently classify real vs. fake songs, combining them yields better performance across short and long songs, showcasing their complementary nature.

* **Potential copyright issue**: The concern was raised as the generative models used to create the dataset may have been trained on copyrighted music. However, our dataset qualifies as fair use under ``17 U.S.C. § 107`` for research purposes. Additionally, using generative models trained on copyrighted data to create datasets is common, and relevant papers have been published in peer-reviewed conferences. Moreover, pairwise similarity checks verified that no real songs have been copied (generated).

* **Possible overfitting on Song Genre**: We analyzed and found that real and fake songs span all genres with significant overlap (figure provided in discussion). Given this overlap and the model’s strong performance, it is clear that the model does not rely on genre classification to detect fake songs.

* **Possible overfitting on Audio Quality (Sample Rate)**: We resample all the real and fake songs to `16 kHz` thus it is not possible to overfit on audio sample rate. We further clarify this in our paper.

* **Dataset integrity**: We evaluated the steps in our dataset creation pipeline to investigate potential issues and their impact. PyAnnotate showed high accuracy for vocal detection, with negligible impact from rare misclassifications. Furthermore, manual analysis confirmed the high accuracy of AI-extracted song styles, with minor issues in vocal type classification. Moreover, as the lyrics are generated using LLMs, there can be no transcription error as no transcription is being done, while pairwise similarity checks verified that no real lyrics have been regurgitated. These evaluations ensure the dataset's integrity while showing minimal impact from any potential issues.

* **Performance drop with increasing $t$ and $f$** : Increasing $t$ (temporal clip size) and $f$ (spectral clip size) reduces the number of temporal and spectral tokens, as indicated in `Eq. 2`. As a result, performance drops with larger $t$ and $f$.

* **Lack of Samples**: We have provided representative samples across all requested categories (Full Fake, Half Fake, and Mostly Fake) and generation platforms (Suno and Udio) in an anonymous link in the comments.
</content>
</thread_object>

</thread>
<thread>
creation date:
1733297616782

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Final Overview (Part 1 of 2)

comment:
We thank the reviewers for their insightful feedback. We are pleased that all of the reviewers found our contribution **good**. We have addressed all the concerns raised by the reviewers. Specifically, two reviewers, `hEps` and `LAdp`, have already acknowledged that all their concerns have been resolved, while the remaining two reviewers have not yet responded. Below, we summarize our contributions and provide responses to the major concerns:

### Contributions
* Introduced the first dataset specifically designed for detecting end-to-end synthetic songs.
* Provided a human benchmark, which is lacking in existing works, alongside traditional AI benchmarks.
* Emphasized the critical role of long-range context in the detection of synthetic songs and highlighted the computational challenges of existing approaches while processing long audios.
* Proposed an efficient model to process long audio samples, which also provides better performance than existing approaches.
</content>
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</thread>


Metareview:
metareview:
This paper presented a dataset and a system for detecting AI generated music, using songs from YouTube and fake songs generated from Suno and Udio. The detection system SpecTTTra uses spectral and temporal patches with a transformer-based classification model. 
In the evaluation, the test sets include unseen algorithms and unseen singers, showing a reduced performance on OOD data. 
Considering that the primary contribution of the paper is providing the first dataset for detecting AI generated songs, this seems as valid accomplishment on its own. The objective of creating a robust detection system is only partly achieved as generalization remains a problem, especially considering later experiments with SkyMusic and SeedMusic.