# SONICS: Synthetic Or Not -- Identifying Counterfeit Songs

arXiv: https://arxiv.org/abs/2408.14080 · HTML: https://mlsanity.com/paper/2408.14080/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 58 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction](https://mlsanity.com/paper/2305.18752/) (2023)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism](https://mlsanity.com/paper/2105.02446/) (2021)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection](https://mlsanity.com/paper/2311.11278/) (2023)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [Efficient Training of Visual Transformers with Small Datasets](https://mlsanity.com/paper/2106.03746/) (2021)
- [End-to-end speaker segmentation for overlap-aware resegmentation](https://mlsanity.com/paper/2104.04045/) (2021)
- [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020)
- [One-class Learning Towards Synthetic Voice Spoofing Detection](https://mlsanity.com/paper/2010.13995/) (2020)
- [Online Detection of AI-Generated Images](https://mlsanity.com/paper/2310.15150/) (2023)
- [STC Antispoofing Systems for the ASVspoof2019 Challenge](https://mlsanity.com/paper/1904.05576/) (2019)
- [Improved DeepFake Detection Using Whisper Features](https://mlsanity.com/paper/2306.01428/) (2023)
- [Masked Modeling Duo: Towards a Universal Audio Pre-training Framework](https://mlsanity.com/paper/2404.06095/) (2024)

## Built on by (most cited first, 21 of 21)

- [Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities](https://mlsanity.com/paper/2503.03983/) (2025)
- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion](https://mlsanity.com/paper/2506.15981/) (2025)
- [Data-Driven Analysis of Text-Conditioned AI-Generated Music: A Case Study with Suno and Udio](https://mlsanity.com/paper/2509.11824/) (2025)
- [AI-Generated Song Detection via Lyrics Transcripts](https://mlsanity.com/paper/2506.18488/) (2025)
- [AI-Generated Music Detection in Broadcast Monitoring](https://mlsanity.com/paper/2602.06823/) (2026)
- [Evaluating Fake Music Detection Performance Under Audio Augmentations](https://mlsanity.com/paper/2507.10447/) (2025)
- [Segment Transformer: AI-Generated Music Detection via Music Structural Analysis](https://mlsanity.com/paper/2509.08283/) (2025)
- [Bias beyond Borders: Global Inequalities in AI-Generated Music](https://mlsanity.com/paper/2510.01963/) (2025)
- [Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning](https://mlsanity.com/paper/2512.00621/) (2025)
- [Fusion Segment Transformer: Bi-Directional Attention Guided Fusion Network for AI-Generated Music Detection](https://mlsanity.com/paper/2601.13647/) (2026)
- [Detecting Musical Deepfakes](https://mlsanity.com/paper/2505.09633/) (2025)
- [TuneGenie: Reasoning-based LLM agents for preferential music generation](https://mlsanity.com/paper/2506.12083/) (2025)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Echoes: A semantically-aligned music deepfake detection dataset](https://mlsanity.com/paper/2603.23667/) (2026)
- [From Talking to Singing: A New Challenge for Audio-Visual Deepfake Detection](https://mlsanity.com/paper/2605.27944/) (2026)
- [HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark](https://mlsanity.com/paper/2606.01686/) (2026)
- [The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions](https://mlsanity.com/paper/2606.14466/) (2026)
- [Beyond Artifacts: Towards Generalizable Synthetic Song Detection via Music-Intrinsic Features](https://mlsanity.com/paper/2606.16612/) (2026)
- [How Much AI Is in This Track? Quantifying the Proportion of AI-Generated Stems in Hybrid Music Mixtures](https://mlsanity.com/paper/2608.07285/) (2026)
- [Assessing AI-generated music detection in real-world broadcast monitoring](https://mlsanity.com/paper/2608.07359/) (2026)

## Abstract (arXiv)

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.