Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning
Authors: Arnesh Batra
Dev Sharma
Krish Thukral
Ruhani Bhatia
Naman Batra
Aditya Gautam
Venue: Accepted by TMLR ()

Abstract: The rapid evolution of end-to-end AI music generation poses an escalating threat to artistic authenticity and copyright, demanding detection methods that can keep pace. While foundational, existing models like SpecTTTra falter when faced with the diverse and rapidly advancing ecosystem of new generators, exhibiting significant performance drops on out-of-distribution (OOD) content. This generalization failure highlights a critical gap: the need for more challenging benchmarks and more robust detection architectures. To address this, we first introduce Melody or Machine (MoM), a new large-scale benchmark of over 130,000 songs (6,665 hours). MoM is the most diverse dataset to date, built with a mix of open and closed-source models and a curated OOD test set designed specifically to foster the development of truly generalizable detectors. Alongside this benchmark, we introduce CLAM, a novel dual-stream detection architecture. We hypothesize that subtle, machine-induced inconsistencies between vocal and instrumental elements, often imperceptible in a mixed signal, offer a powerful tell-tale sign of synthesis. CLAM is designed to test this hypothesis by employing two distinct pre-trained audio encoders (MERT and Wave2Vec2) to create parallel representations of the audio. These representations are fused by a learnable cross-aggregation module that models their inter-dependencies. The model is trained with a dual-loss objective: a standard binary cross-entropy loss for classification, complemented by a contrastive triplet loss which trains the model to distinguish between coherent and artificially mismatched stream pairings, enhancing its sensitivity to synthetic artifacts without presuming a simple feature alignment. CLAM establishes a new state-of-the-art in synthetic music forensics. It achieves an F1 score of 0.925 on our challenging MoM benchmark, significantly outperforming the previous SOTA's 0.869 on the same dataset. This result demonstrates superior generalization to unseen generative models. Furthermore, CLAM scores 0.993 on the popular SONICS benchmark, confirming its effectiveness and setting a new performance standard.

Naman Batra² Published in Transactions on Machine Learning Research (11/2025)
Melody or Machine: Detecting Synthetic Music with Dual- Stream Contrastive Learning
Arnesh Batra
Indraprastha Institute of Information Technology Delhi (LIT-Delli), India
Dev Sharma'
dev23189Gmätd. ac.
Indraprastha Insiitute of Information Technology Delhi (IIT--elltt), India
Krish Thukral?
krish. brish.39fe.00see000000mmmmmmmmmp edu
Manipal University Jaipur, Rajasthan, India
Ruhani Bhatia
ruhani234500hiiddd.cu
Indraprastha Institute of Information Technology Delhi (IIT--ellii) India
naman.batra hummammbbrrr.ug22388ssta...
Netaji Subhas University of Technology (NSUT), Delhi, India
Aditya Gautam'
adilyaa204333iii
Indraprastha Institute of Information Technology Delhi (III-- (UT--Dlhii), India
Reviewed on OpenReview: https: // openreview. net/ påfz id= Ufwes0o2e3
Abstract
The rapid evolution of end-to-end AT music generation poses an escalating threat to artistic authenticity and copyright, demanding detection methods that can keep pace. While foun- dational, existing models like SpecTTTra falter when faced with the diverse and rapidly advancing ecosystem of new generators, exhibiting significant performance drops out- of-distribution (OOD) content. This gencralization failure highlights critical gap: the need for more challenging benchnarks and more robust detection archilectures. To address this, we first introduce Melody or Machine (MoM), a new large-scale benchmark of over 130,000 songs (6,665 hours). MoM the most diverse dataset to date, built with a mix of open and closed-source models and curated OOD test designed specifically to foster the development of truly generalizalle detectors. Alongside this benchmark, we introduce CLAM, novel dual-stream detection architecture. We hypothesize that subtle, machine- induced inconsistencies between vocal and instrumental elements, often imperceptible in mixed signal, offer a powerful tell-tale sign of synthesis. CLAM is designed to test this hy- pothesis by employing two distinct pre-trained audio encoders (MERT and Wave2Vec2) to create parallel representations of the audio. These representations are fused by a learnable cross-aggregation module that models their inter-dependencces. The model is trained with dual-loss objective: a standard binary cross-entropy loss for classification, complemented by contrastive triplet loss which trains the model to distinguish between coherent and arti- ficially mismatched stream pairings, enhancing its sensitivity to synthelic artifacts without presuming a simple feature alignment. CLAM establishes new saate-of-the-ans in syn- thotic music forensics. It achieves an F1 score of 0.925 on our challenging MoM benchmark, significantly outperforming the previous SOTA's 0.869 on the same dataset. This result demonstrates superior generalization to unseen generative models. Furthermore, CLAM scores 0.993 on the popular SONICS benchmark, confirming its effectiveness and setting new performance standard.

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Published in Transactions on Machine Learning Research (11/2025)
1 Introduction
The rapid sophistication of end-to-end Al music generators has created a new class of synthetic media that is often indistinguishable from human created recordings. This development, encompassing everything from deepfake voice clones to fully synthesized songs, presents urgent and evolving challenge. The core problem is not one of artistic judgment distinguishing, for example, avant-garde human compositions from Al but technical one: reliably identifying the subtle, digital artifacts of the generative process itself. Failure to do so threatens to erode intellectual property rights, undermine artistic authenticity, and create media environment where the line between human and machine creation irreparably blurred.
Table TTeaTTTra OOD F1 Scores
Dataset
Total Samples
F1 Score (%)
Riffusion
7,057
53.46
Yue
5,278
68.80
Voice clone
1,166
50.94
Suno
48
64.58
Table 2: Quantitative Comparison of Fake Song Datasets
Avg. Length (sec)
Total
Dataset
Language
Algos # Real Songs
Fake Songs
Hours
FSD
Chinese
216.00
200
450
26
SingFake
Multi
13.75
634
671
58
CtrSVDD
Multi (no English)
4.87
14
32,312
188,486
307
SONICS
English
176.03
5
48,090
49,074
4,751
MoM (ours)
Multi (82% English)
195.99
9
65,475
64,960
6,665
While foundational, carly deepfake detection efforts were constrained by datasets with limitod scale, di- versity, and audio fidelity (Zang al.] 2024b a Liu et al. 2021; Tae et al.] 2021; Xu et al. 2022). More recent. benchmarks like SONICS (Rahman et al., 2025) represented
advancement, providing over 97,000 longer songs, including end-to-end synthetic tracks from platforms like Sund and Udid and introduced the SpecITTra (Rahman et al. 2025) model for capturing long temporal dependencies. Nevertheless, SONICS exhibits drawbacks such as a male vocal bias, a lack of multilingual content, and irrelevant, prompt attributes. The rapid progress in song generation models underscores the necessity for more diverse datasets and robust models capable of elfectively handling critical limitations: a reliance on only a few primary generation models and significant demographic biases (o.g., predominantly male, English language vocals). This lack of diversity creates critical vulnerability. Models trained these datasets learn to identify superficial, generator specific patterns, resulting in dra- matic failure of generalization when tested against out-of dis.iiiiitiitoonnnnn (OOD) content from unseen models. As we demonstrate, existing state-of-the-are detectors exhibit a sharp performance drop in these real world sccnarios (see Table I proving them unreliable for practical application.
weoof-distributiin samples. However, their utility hampered by
To confront this generalization crisis, we introduce MoM (Melody or Machine), a benchmark of 130,435 songs. Its key innovation is diversity, featuring a mix of open and closed-source generators (Yuan et al, 2025 Ziqian et al. 2025), dedicated out-of-distribution (OOD) test set to ensure generalization, and 65,475 real songs including human covers to challenge models against natural stylistic variations. Unlike previous work, which uses the same models for training and testing, MoM's training set includes Suno v3.5, Suno v2, Diffrhythm (Ziqian et al. 2025), and Udio v1.5, while the test set features Riffusion (FUZZ-1.0 Suno v4, Suno v3, Yue (Yuan et al. 2025), and audio from voice cloning methods.
https://xwv.cr
https://www.ude
https://www
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We also propose CLAM (Contrastive Learning for Audio Matching), novel dual-stream architecture that operates on mixed audio. Built on the hypothesis that AT disrupts track's s internal consistency, CT.AM uses two pretrained encoders, MERT şi et al., 2024) and Wave2Vec2 (Baevski et al.) 2020), to generate parallel feature representations. A hybrid loss function then combines a binary classifier with a contrastive Triplet Loss (Schrolf et al. 2015), which pulls representations of the same authentic track closer while pushing those from different tracks apart. This trains the model to recognize the inherent consistency of real recordings, a property we posit is disrupted by synthesis.
Our main contributions as follows:
A New, Large-Scale Benchmark for Generalization (MoM): We release the most diverse syuthetic music dataset date, featuring wide array of generative models and dedicated out-of- distribution test set designed to measure real-world robustness.
A Novel Dual-Stream Detection Model (CLAM): We propose a now paradigm that analyzes parallel leature representations from mixed audio and trained with a contrastive objective identify subtle generative artifacts.
Sta Performance and Analysis: We demonstrate through extensive experiments that CLAM significantly outperforms existing models, achieving a new statc-of-the-art Fi-score of 0.925 on our challenging MoM benchmark, and provide a comprehensive analysis the generalization failures of current detectors.
Table 3: Qualitative Comparison of Datascts
Closed Source Models
Diverse Style Soogss
Open Source Models
Fully Fake Songs
Toxt Lyrics Songs
Multilingual Sougss
Dataset
FSD (Xie et al. 2023
SingFake Zang et al.
2024b)
CtrSVDD Zaag 2024a
SONICS (Rahman
2025
MoM (ours)
2
Related Works
The rapid advancement of end-to-end generative models has led to surge in Al-generated music, capable of producing entire songs with vocals, lyrics, instrumentation, and stylistic muances. Detecting such synthetic compositions requires datasets and models that can capture both low-level audio artifacts and high-level musical coherence. In this section, we first survey the major public datasets designed for song deepfake de- tection, then turn to the spectrum of modeling strategies ranging from classical spectogram- architectures.
approaches
2.1 Audio Datasets for Synthetic Song Detection
Early efforts in synthetic song detection relied on small, narrowly scoped datasets, but recent benchmarks have begun to emphasize greater scale and diversity.
SONICS It the current Prate-of-the-Ae Dataset consisting of large collection of over 97,000 full-length tracks(475] hours of audio) evenly split between real recordings and synthetic songs generated by platforms
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such Suno (v2-v3.5) and Udio (v32, v130). Crucially, SONICS's songs are quite long (average of 176 seconds), supporting the modeling of long-range musical and lyrical patterns. It also includes the text lyrics of the songs, which can aid future research. However, SONICS also has clear limitations since it consists only of English songs (mostly featuring male vocals) with audio processed at a fixed 16 kHz rate, and its synthetic tracks come exclusively from Iwo Al models (Suno and Udio), with the Half Fake tracks being only generated from one model- which means one compositional scenario (real lyrics with Udio) missing, narrowing the variety within that category.
Other Datasets SingFake offers 58 hours of paired real and synthetic vocal clips across five languages and 40 singers, using real instrumental backings to aid in artifact delection. However, it lacks fully synthetic lyrics or instrumentals. CtrSVDD builds upon this by providing 308 hours of controlled SVDD content (over 220,000 clips), enabling fine-grained manipulation through paramcterized synthesis and metadata through it, centering on vocals rather than full-song generation. This focus on partial clips or vocals, while useful, may not capture the long-range compositional narrative and complex interactions between elements present in full-song tracks, which is a key challenge for detection. Meanwhile, FSD (Fake Song Detection) (Xie et al, 2023 is a Chinese language benchmark comprising 200 real and 450 fake songs produced via five modern synthesis and conversion techniques. Its difficulty for speech-based detectors underscores the need for music- specific modeling, though its limited linguistic and methodological scope hinders broader applicability.
Although prior datasets have laid important groundwork for synthetic music detection, they often lack the diversity in generation pipelines and audio characteristics needed for models to generalize effectively to emerging AI content. Our contribution includes the Melody or Machine (MoM) datasct, specifically designed with extensive source and variation coverage to bridge this gap.
2.2 Modeling Approaches
Detecting Al-generated music spans a broad range of techniques, from classical signal transforms to deep neural architectures. Early methods (Pour et al.] 2014 Wani et al., 2024a b. Pham et al.] 2024 Yi et al. 2023 convert raw waveforms into time frequency representations such as STFT, CQT, MFCCs, or gammatonegrams. These are typically fed into convolutional or recurrent networks to identify synthesis artifacts. While effective at highlighting low-level inconsistencies, these approaches often fail to capture long-term musical structure and are sensitive to genre and production variations.
To address these limitations, recent mothods incorporate long-context modcling using attention mechanisms and spectro-lemporal lokenization. For example, models like SpecTTTra (Rahman et al. 2025) (SOTA on SONTCS) decompose spectrograms into patch-based tokens and apply transformers or memory- augmented RNNs to model global coherence in lyrics, rhythm, and instrumentation. These techniques show promise in detecting higher-order anomalies but risk overfitting specific generator signatures, limiting their robustness across domains.
Another line of work leverages self-supervised audio encoders Sharma & Gupta 2025 Guo et al. 2024 Phukan et al.] 2024a|b) bb such as MERT, wav2vec2, HuBERT (Hsu et al. 2021 ). and Audio Spectrogram Transformer (AST) (Gong et al. 2021). These models are pretrained on large speech or music corpora to learn rich acoustic representations. When fine-tuned for deepfake detection, they often outperform purely supervised models. However, the gap between speech and music, characterized by wider pitch ranges, melodic structures, and vocal expression, necessitates domain-specific adaptation or joint pretraining across modalities.
In summary, the field has progressed from handcrafted audio features to sophisticated neural architectures with self-supervised learning and structured modeling. However, detecting fully end-to-end generated songs blending vocals, lyrics, instrumentation, and style remains an open challenge. Our work builds on these insights by leveraging pretrained encoders combined with contrastive training, achieving state-of the--r performance in Al-generated music detection.
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3 Methodology
3.1 The Melody Machine (MoM) Dataset
This section details methodology for creating benchmark to detect specific types of Al-generated music, ranging from partial manipulations like voice cloning to fully synthetic, end-to-end generated songs. To address the critical generalization failures of existing detectors, we developed the Mclody or Machine (MoM) dataset, & large-scale benchmark designed to reflect. this diversity. MoM provides 130,435 audio tracks organized into three operational tiers: Real, Fully Fake, and Mostly Fake, which together enable a nuanced evaluation of detection models. The dataset incorporates a wide array of both proprictary and open-source generators, including Suno (v2-v3.5), Udio (v1.5), Riffusion, Diffrhythm (Ziqian al., 2025) and Yue (Yuan al. 2025
3.1.1
Real Songs
The Real subset comprises 65,475 human-created tracks, providing robust ground truth for authentic audio. This includes 47,971 original songs sourced from YouTube, with metadata linked from the Genius Lyrics Dataset (Lim & Benson. 2021). : and 17,504 high-quality human-performed covers. The inclusion of covers is a key design choice, as it challenges models to distinguish genuine AI artifacts from the natural variations in timbre, arrangement, and style inherent in human creativity.
3.1.2 Fully Fake Songs
This tier contains 53,922 tracks generated via a structured prompting pipeline designed to ensure musical relevance and stylistic diversity, moving beyond simple random generation. To achieve this, employed three distinct prompting strategies.
Type A: Inspired by Existing Songs To simulate a common human creative process, prompts were generated by reimagining existing song titles in a new musical style, with genres sampled from a curated list of 163 options derived from the FMA dataset (Defferrard al. 2018 2017).
Type Curated by Musical Attributes We created structured taxonomy of musical features: genre, mood, lempo, key, instrumenlation, and production style. We then programnattiaalyy gener- ated thousands of unique attribute combinations. These were refined using Gemini 2.0 Flash (Google) 2025) to translate them into fluent, natural-language prompts suitable for generation systems.
Type C: Communit--Soureed To capture 'in-the-wild" usage, we sourced high-frequency prompts directly from public, state-of-the-art generative music platforms, enhancing the dataset's 's robustness against practical edge cases and organic user trends.
3.1.3
Mostly Fake
This subset simulates partial synthetic manipulation, common real-world scenario.
Type A: Real Lyrics + AI Audio - We sourced tinestamped lyrics from commercial songs and used a fine-tuned BERT-based classifier (Devlin el al., 2019) to predict a stylistically appropriate genre for the lyrical content. The lyrics and predicted genre were then fed to models like Diffrythm and Yue to generate coherent audio.
Type B: Voice Cloning This subset of approximately 1,000 samples addresses another prevalent deepfake scenario: Al-generated vocal covers. Here, known artist's vocal timbre is synthetically applied to new performance (using different lyrics or instrumentals). These samples, sourced from public platforms, represent contemporary voice cloning practices and test model's ability to detect vocal synthesis specifically.

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Table 4: This table shows the number audio samples used for araining/vallidtee and testing, organized by model. Models marked in red are closed-source (e.g., Suno, Udio, Riffusion, Voice Clone) and models marked in blue are open-source (e.g., Yue and Diftrythm).
Model
Train / Validation
Test
Suno 3.5
23695
Udio 1.5
19500
Difirythm
4606
Suno
110
Suno
48
Suno 3
3512
Riffusion
7057
Yue
5278
Voice Clones
1166
Total
47911
17061
3.1.4 Dataset Evaluation
The MoM dataset improves prior benchmarks like SONICS with broader genre, language, and vocal diversity, enabled by more expressive prompt pipeline and a mix of open and closed-source models. It supports realistic, end-to-end song generation with coherent vocals and instrumentation, reflecting new AT music trends.
To quantitatively measure and benchmark the perceptual quality of the generated audio, we established human evaluation platform. The evaluation was conducted with general, non-expert participant demographic, primarily university students and their networks, to gauge broad perceptual quality relevant to real-world content moderation scenarios. Participants performed blind, head-to-head comparisons where the A/B pairings were randomized across the entire pool of models to produce a general perceptual Icaderboard.
The central question posed participants was "Which song sounds better overall?". To guide this subjective assessment and ensure composite perceptual judgment, we instructed evaluators base their choice on four key aspects of the audio:
Overall Audio Quality: This included listening for clarity, absence of distortion or strange artifacts, and the overall fidelity of the track.
Realism: Judges were asked to consider how natural and convincing the instruments, vocals, and the complete soundscape felt.
Musicality: An optional factor, this pertained to whether the melody was catchy, the harmony was pleasing, and the musical structure was coherent.
Lyrics: Also optional and depending on the sample, this involved evaluating the lyrics were clear,
coherent, and rell-integrated into the song.
These human judgments were then aggregated into a quantitative ranking using the ELO rating system, adapted from chess, which dynamically scores each model based its win/loss outcomes. The ELO rank- ings were calculated exclusively based the mandatory "Which song sounds better overall?" question. The resulting ELO score thus reflects the overall perceptual preference defined by our multifaceted voting instructions (clarity, realism, fidelity, and optionally musicality/lyrics)
Our evaluation shows that MoM's synthetic tracks achieve a higher average ELO score (1016.35) than those in the SONICS benchmark (894.60), indicating their superior perceived quality. Most notably, the perceived quality of our top synthetic models, like Riffusion (ELO 1105.58) and Udio (ELO 1093.34), was so high that they ranked above authentic human-created songs (EI.O 1032.84) by listeners. This underscores

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that the line between human and machine creation is blurring, motivating the need for more sophisticated detection methods. Our dataset emphasizes generalization by incorporating curated test set composed of diverse out-of-distribution (OOD) samples, including Riffusion (FUZZ-1.0), Yue, Suno 3, Suno 4, and Voice Clones. contrast, prior works primarily evaluate on variants of similar models such as Suno and Udio, which leads overly optimistic results that do not reflect real-world generalization as shown Table 1.
Table 5: Human evaluation results using ELO-based ranking. Participants voted on which sample 'sounds better, considering clarity, absence of artifacts, overall fidelity, realism, and optionally musicality and lyrical coherence. The resulting ETO scores thus capture a composite perceptual preference. See Appendix for details.
Model
ELO Score
Riffusion (ours)
1105.58
Udio (ours)
1093.34
Real
1032.84
Suno (ours)
1013.76
Voice Clones (ours)
1007.23
Yue (ours)
958.37
Diffrythm (ours)
934.14
Suno (SONICS)
901.75
Udio (SONICS)
887.44
Average (Ours)
1016.35
Average (SONICS)
894.60
3.2 CLAM: A Probabilistic Approach to Learning Audio Authenticity
We introduce CLAM (Contrastive Learning for Audio Matching), architecture designed not find simple spectral artifacts, but to learn the holistic signature of musical authenticity. CLAM operates the full, mixed audio signal, a key feature for practical, real-world applications.
Freeze
Finetune
MERT
ConviD
Concat
Linear
Drapout
Linoar
Probabiity
Alignment Loss
Music
Mali Heae Ateemoon
Compress
DonviD
Label
BCE Loss
WavZVec2
Figure 1: Overview of the CLAM (Contrastive Learning for Audio Matching) architecture. This two-encoder model processes instrumental and vocal embeddings (from sources like MERT and Wav2Vec2), incorporates a Weighted Cross- Aggregation (WCA) module, and dual-loss objective.

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3.2.1 Core Intuition: A Probabilistic View Musical Authenticity
An authentic musical recording a sample from a complex, structured joint probability distribution, P(V, M), where V and M represent. the vocal and instrumental elements. This distribution is governed by the physics of acoustics and the grammar of music theory. Our central hypothesis that Al generators learn flawed approximation, P(V.M), Our goal is to train detector that is maximally sensitive to the Kullback-Leibler (KL) divergence (Hershey & Olsen 2007) between these two distributions:
E P(x) log P(x)
DRL(PIIP)
(1)
P(x)
#E(V,M)
We posit this divergence is most pronounced in the subtle, high-order statistical dependencies between musical elements.
Manifestations of the Modality Gap. This discrepancy, or modality gap, manifests in musically sig- nificant ways. For instance, Al generators may break the assumption of conditional dependence; human singer's vocal timbre changes with pitch, but an Al might model these as conditionally independent given the melody, creating an unnatural sound. Similarly, the rhythmic pocket of a human performance, a structured deviation from a perfect grid, a complex dependency that. Al often fails to replicate, resulting in rhythms that are either sterilely quantized exhibit unstructured, random timing.
The Authenticity Manifold. Geometrically, we frame this using the Manifold Hypothesis. We posit that feature vector pairs (dm, dy) extracted from authentic songs lie on or near a low-dinensional authenticity manifold, A, embedded within a high-dimensional joint feature space Id x IR.. Synthetic songs, drawn from the flawed distribution P, produce feature pairs that lie off this manifold. CLAM's novelty is its design to first learn the geometry A and then identify any signal that deviates from it.
3.2.2 Dual-Stream Feature Projections
To perceive this manifold, CLAM projects the mixed audio signal, S, onto two distinct, complementary feature subspaces using pretrained encoders acting as projection functions, Φar and Φv:
Aware-Aware Projections (Φas): We use MERT Li et al. 2024 to generate a music-centric view, dmusic = dm (S), capturing structural information like harmony and rhythm.
Timbral-Acoustic Projections (øv): We use Wave2Vec2 [Bevvski et al. 2020) to generate a view emphasizing fine-grained timbral and articulatory textures, dyacal = (S)
3.2.3 Weighted Cross- Agogrgggggg (WCA) Module
The WCA module distills the raw, multi-layered encoder outputs into compact, fixed-size embeddings.
1. Learned Layer Aggregation. To create an optimal representation, we compute learnable weighted sum across all L encoder layers. For a set of layer outputs J the aggregated map is Lu (() where the weights w, are the learned parameters of a ID convolution (O'Shea & Nash M Elm i=
2015 with a kernel size of 1.
2. Intra-Stream Self-Ar Attention. Before fusion, a multi-head self-attention mechanism Vaswani et al. 2023) refines each aggregated stream. By computing attention scores between Query, Key, and Value projec- tions of the input sequence, this step builds a context-aware representation that focuses the most salient musical or vocal phrases.
3.2.4 Learning the Manifold's Geometry via Contrastive Metric Learning
Our learning objective hybrid of discriminative classification and probabilistic metric learning. A stan- dard Binary Cross-Entrops (BCE) loss on the concatenated embeddings drives the final classification. We
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supplement this with powerful inductive bias via the Triplet Loss (Schroff et al. 2015) which acts geometric regularizer. This approach can be formally understood through the lens of an energy-based model (EBM). We define an energy function E dy) parameterized by the model weights 0, which should assign low energy to authentic, internally consistent pairs and high energy to all other pairs. The goal to learn 0 such that the Gibbs-Boltzmmnn The Triplct Loss powerful method shape this energy surface without computing the intractable par- tition function Zg. It enforces the margin-based constraint that the energy of an authentie, matched pair (positive) must be lower than the energy of mismatched pair (negative) by at least margin
distribution (Rowlinson* * 2005), assigns high probability to authentic pairs.
Ea + <& Ea
(2)
where the energy E is instantiated as the squared Euclidean distance. The loss function is thus:
Liiplet = max (0, Fa p) Ea n) + a
(3)
We apply this loss only to real songs in batch, where for given real song
Anchor (a): The music-centric embedding, d [t.
Positive (p):
The vocal-centric embedding of the same song, doeallti [t].
Negative (n): The vocal-centric embodding from different real song j.
This objective forces the two views of any real song to have low energy, making the authenticity manifold A compact, low-energy region. The total loss,
Liotal = LBCE + Liriplet
(4)
jointly optimizes for classification accuracy and latent space structure that reflects the probability distri- bution of authentic data. The contrastive loss acts as a strong regularizer, simplifying the classifier's task scparating the low-energy manifold from the high-energy space around it.
4
Experiments and Results
4.1 Experimental Setup
All models were trained on an NVIDIA RTX 4060 Ti 16GB GPU. We used the AdamW optimizer (Loshchilov & Hutter 2019) with learning rate of le-4, a batch size of 128, and an embedding dimension of 512 for 50 epochs. For reproducibility, all experiments were run with 5 seeds and the results provided are average across those them, and all code is provided in the supplementary material. Audio samples were trimmed to seconds and resampled to 24 kHz to balance computational load with the preservation of high-frequency details.
Our primary evaluation metric is the F1 score, chosen for its robustness in measuring performance imbalanced or challenging OOD test sets where simple accuracy can be misleading (Christen et al. 2023 While multiple runs for statistical significance tests were computationally prohibitive given the dataset's 's scale, we provide detailed ablation studies to rigorously validate our contributions. For experiments involving the contrastive loss, the weight was held at 0.5 based on preliminary validation performance which was carried out the range of 0.1 to I values of X
4.1.1 Main Results on the MoM Benchmark
The primary goal of our work is to address the failure of current models on diverse, out-of-distributiin data. We therefore present our main results on the challenging MoM benchmark, which was specifically designed test this generalization capability. As shown in Table 7 the previous statc-of-the-art model, SpeeTTTra, achieves an F1 score of 0.869 on MoM. Other recent basclines, MiO (Phukan al.) (024a) and Dien-HierNet (Yang al. 2025), achieve 0.872 and 0.896, respectively, highlighting the difficulty of the benchmark.
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Table 6: F1 Score performance on the SONICS dataset.
Method
F1 Score
TracTTTTaa (120 sec)
0.972
CLAM (No Aligument Loss)
0.987
CLAM (Triplct Loss)
0.993
Table 7: Performance comparison on MoM dataset.
Method
Accuracy (%)
F1 Score (%)
State of the Art Models
SONICS SpeeTTTra-a (Rahman et al. 2025
87.2
86.9
MiO Phuukn et al.
2024a
88.4
87.2
Poin- HierNet YYang et al. 2025
90.3
89.6
Unimodal Only
AST (No WCA)
81.6
80.1
HUBERT (No WCA)
82.0
78.9
MERT (No WCA)
83.5
81.3
Wav2Vec2 (No WCA)
82.7
80.2
MERT Only
86.1
85.3
Wav2Vee2 Only
83.1
84.6
Multimodal (No Alignment Loss)
CLAM (No Alignment Loss)
88.8
87.5
Multimodal + Alignment Losses
CLAM (MSE Loss)
90.1
89.9
CLAM (Huber Loss, 8=1.0)
90.3
90.1
CLAM (Cosine Loss)
90.8
90.3
CLAM (Triplet Loss)
93.1
92.5
In contrast, our proposed CLAM model with Triplct Loss achieves a new statc-of-the-art F1 score of 0.925. This significant improvement of nearly points over SpecTTTra and 3 points over the strong Poin-HierNet baseline demonstrates the superior generalization capabilities of our dual-stream, contrastive approach when faced with true OOD challenge.
4.1.2 Ablation Studies
To deconstruct the sources of this performance gain and validate our design choices, we conducted series of ablation studies.
The unimodal baselines, using only MERT or only Wave2Vec2, achieve F1 scores 0.853 and 0.846, respec- tively (Table 7 MERT Only/Wor Wec2 Only). Our dual-stream CLAAM model, even without the alignment loss, achicves an F1 score of 0.875. This substantial improvement confirms that analyzing the audio through two complementary feature streams captures richer information than either stream can alone, validating our core architectural hypothesis.
Another critical question is whether the alignment loss component provides meaningful benefit. By com- paring the CLAM model without this loss (0.875 F1) to the full model with Triplet Loss (0.925 F1), we see a clear and significant performance increase. This demonstrates that explicitly training the model to structure the embedding space by pulling representations of the same real song together and pushing others apart is highly effective. It confirms our intuition that modeling the internal consistency of authentic recordings is a powerful signal for detecting A A-ggenerated content.
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We validated our performance gains using two-sided McNemar's tests, with full methodological details and contingency tables provided in the Appendix. The results show that our best model, CLAM (Triplet Loss), achieves highly significant improvements over both SpecTTTrra and Poin-HierNet (p < 0.001). The dual- stream CLAM (No Alignment Loss) also significantly outperforms the strongest unimodal baseline, MERT Only (p < 0.01). Moreover, incorporating the aligument loss yields a highly significant boost, with CLAM (Triplet Loss) outperforming CLAM (No Aligument Loss) (p < 0.001). Overall, these tests confirm that our architectural choices and the Triplet aligument loss lead robust, statistically reliable gains. Detailed results are included in the Appendix.
4.2 Performance on the SONICS Benchmark: A Saturation Test
To ensure model also performs well existing benchmarks, evaluated it on the SONICS dataset. As shown in Table G CL.AM achieves near-perfect F1 score of 0.993, marginally outperforming the non- contrastive variant (0.987) and the SpecTTTraa- model (0.972).
However, the near- scores achieved by multiple top models on this dataset indicate that it has become 'saturated." is no longer sufficiently challenging to differentiate the capabilities of advanced architectures. This observation further underscores the novelty and necessity of our more diverse and challenging MoM benchmark for driving future progress in the field.
5 Discussion and Conclusion
In this paper, we confronted the critical challenge of out-of-distributio generalization in Al-generated music detection. We introduced MoM, the most diverse public benchmark to date, specifically designed with a dedicated OOD test set drive the development of truly robust models. MoM's inclusion of varied gen- erators, multiple languages, and human cover songs provides a more realistic and challenging evaluation standard for the field. Alongside this benchmark, we proposed CLAM, a novel dual-sttream architecture. Instead of relying fragile, generator-specffic artifacts, CLAM is designed to detect subtle inconsistencies between parallel feature representations of single mixed audio track. Our experiments provide strong support for this approach: the dual-stream model consistently and significantly outperforms unimodal base- lines. Furthermore, the use of a contrastive Triplet Loss measurably improves performance by encouraging the model to learn the internal consistency of authentic recordings. This method provides a more principled way to identify synthetic content than relying on simple spectral pattern matching. By achieving a new stale-of-the-art F1 score of 0.925 on the challenging MoM benchmark, this work not only delivers a superior detection model but also establishes new, tectors. By releasing MoM, we hope to catalyze shift in focus from in-domain performance to the far more critical goal of roal-world generalization. Our Main contributions is filling the gap in the existing works by making a novel dataset and showing that understanding the semantics music are very important in this problem.
rigorous methodology for evaluating synthetic music de-
6 Limitations
Our primary limitation stems from the relentless pace of innovation in AI music generation. The generative models used in this study represent the thtee-tfthhe-art today, but new architectures are constantly emerging, This "arms race' means any detection model, including ours, faces continuous risk of obsolescence as new, more sophisticated generators produce music with different, more subtle artifacts. Consequently, the MoM dataset will require periodic updates and our model will need retraining to remain effective against future threats. A second significant challenge the computational cost associated with our model's size and complexity. Additionally, our dataset prodominantly English (82%), and the model's behavior on other forms of computer-generatee music (e-g., algorithmic composition) is untested.
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7 Broader Impacts
The MoM dataset and the CLAM model create noteworthy positive societal outcomes through a greater capacity for the detection of Al-created music. For example, content platforms, rights holders, as well as forensic analysts, gain the ability to identify synthetic tracks. This supports the security of artist intellectual property and the maintenance of trust in music distribution. MoM's detailed annotations plus song repre- sentations with atlention to multilingualism and gender balance give support to the creation of moderation tools in the future. These tools can offer more fairness and inclusivity across the music industry. At the same time, a large, good corpus of actual plus synthetic songs could help in the training of deepfake generators that appear more real.
The focal point of the project is to label music on popular platforms, so that the music is presented to the listeners with full transparency about the production of the music they are consuming, making them aware of authenticity as well as the ability of AI. Music artists may benefit from this labeling as well, as it prevents faking AT generated music to be posed as human made which provides an unfair advantage by displaying level of human element the listener, this is similar to the issues faced by graphic designers who had their livelihood affected by the advancements in AI image generators which would have been prevented to extent, had a labelling system been introduced sooner.
Full dependency on automated detectors presents risk of false positives or unfair treatment of genres or vocal styles not well represented in the data as well as potentially harmful to smaller artists their music is incorrectly labelled as synthetic. Another point of potential issues affects artists who either unknowingly use samples generated with AI or those who are physically incapable of some part of production- cither vocals or instrumentals and want use AI complete their work. To lower those risks, a release strategy for the full dataset with controls is good idea.
8 Ethics Statement
Our rescarch was conducted with careful considcration for ethical implications, focusing on responsible data sourcing and transparency.
Data Sourcing and Licensing The Real songs and Mostly Fake Type (voice cloning) samples are sourced from YouTube. To respect copyright, do not host distribute this audio; we provide only the original YouTube links for research replication. All AT-generated songs created for the MoM dataset will be released on Hugging Face under a Creative Commons CC BY-NC 4.0 license, permitting non-commercial research use. We acknowledge that some data was generated using publicly accessible commercial AI tools. This work uses the generated output for non-commercial, academic research with the explicit goal of ad- vancing deepfake detection and promoting authenticity a protective and beneficial application. This aligns with established research practices for creating evaluation benchmarks (Shin et al., 20224] Yang et al. 2023 Wang et al.) 2025). We recognize the evolving legal landscape surrounding Al-generated content and have acted in good faith to create resource for positive research outcomes.
Dual- Mitigation There is an inherent dual-use risk that dataset of synthetic music could be used to train more effective generative models. To mitigate this, the full MoM dataset will be released under a controlled access policy to verified researchers, prioritizing its use for defensive applications like deepfake detection.
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A Technical Appendices and Supplementary Material
A.1 Dataset Description
For our experiments, we curated diverse dataset comprising both real and synthetic songs sourced from several state-of-the-art generative audio models. The dataset spans a wide variety of generation techniques, quality levels, and stylistic characteristics, enabling comprehensive evaluation of detection and generalization performance.
A.1.1 Composition and Distribution
The synthetic portion of the dataset includes samples from the following models:
Suno v2 110 samples
Suno v3 3,512 samples (Test only)
Suno v3.5
23,695 samples
Suno v4 48 samples (Test only)
Udio v1.5 19,500 samples
Diffrythm 4,606 samples
Riffusion 7,057 samples (Test only)
Yue 5,278 samples (Test only)
Voice Cloning 1,166 samples (Test only)
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We further categorize the synthetic songs based on the degree of AI synthe- Fake-Type Categorization.
sis:
Fully Fake: Suno (v2, v3, v3.5, v4), Udio v1.5, Riffusion
Mostly Fake: Yue, Diffrythm, Voice Cloning
This structured labeling allows systematic evaluation under varying levels of generative realism.
A.2
Human-AI Al Perceptual Benchmark
To benchmark our detection models against human perceptual judgment, we developed blind listening evaluation platform.
Audio Model Showdown
Audio Showdown! retready astonishingly realistic? Cast your crucial vote and help shope the live rankings of the competing models! Your ears are the final judgel +
samples
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Figure 2: Interface of the Song Arena platform on Huggingface.
A.2.1 Evaluation Setup
Participants are presented with randomized audio pairs and asked: 'Wiic song sounds more realistic?* Fach pair falls into of two categories:
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Audio Model Showdown
overall It's time for the uttimate Audio Showdown! ? Get ready to listen to tvo samples and tell us: which one delivers better overall quality? Which sounds astonishingly your help shape live rankings competing
the final judge! T
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Live ELO Rankings
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Figure 3: Leaderboard based on Elo scores from human evaluations on the Song Arena platform. Higher scores indicate stronger preference by listeners.
Intra-Dataset: Both songs are drawn from the same dataset but different generation models.
Inter-Dataset: Songs are drawn from entirely different datasets (e.g., MoM vs. external bench- marks like SONIOS).
A.2.2 Model Elo Ranking from our website
To quantitatively assess perceived audio realism from human evaluations, we compute an Elo rating basod on aggregated pairwise comparisons our platform. Higher scores indicate greater preference by listeners. Our proposed Riffusion-basse model achieves the top human realism score.
Table 8: Elo Scores from Human Evaluation
Model
Elo Score
Riffusion (ours)
1105.58
Udio (ours)
1093.34
Real
1032.84
Suno (ours)
1013.76 76
Voice Clones (ours)
1007 23
Yue (ours)
958.37
Diffrythmm (ours)
934.14
Suno (SONICS benchmark)
901.75
Udio (SONICS benchmark)
887.44
Interpretation:
The Elo- based analysis reveals that multiple synthetic models (notably our versions of Riffusion and Udio) surpassed human-composed songs in perceived realism. This demonstrates the increasing difficulty of AI song detection and motivates the need for sophisticated modality-alignman based detection methods.
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Fully Fake Prompt based (Suno, Udio, Riffusion)
Partial Fake Yue, DiffRythm, YouTube
Type inspired By
Type Real Lyrics Faka Audio
Greene an english song inspired by the rong My Flunkue frromme in the style Flecuuacttii
[Mer Cartonn
Crease an ongiiah song insprrd by the serg Leave The Word Behind Raclo Edit, in ztls of Free Jazz
Jetyy' glaa viee Mosie Deuenall
Emitioona Piano
Type Musical Prompt
NealLarice
cumpostiion
gitchricc holograph Productions neanfattine
lister repurpose revolutionary methodoloogies
Yue/ / DiffRythm
revolutionary services
Type C- Randomised Prompts
Type B Volce Cloning (Real Lyrics Real Singer)
A Alorgg ibout within Chich the lave my a b happy birthdaa,, soul, funk, disco
svollatte The sudio samples penerdeed using patning real, punlidty where the singerss vocal character sties are repiccated the through mocess
categnryy invorves dloning the
create frodday stsla, ar sim voices, blenda dements slectronis dance music with rock, punk, and h prhcp lifluences, incorpo aoes heavy use synchesiees, br broakbeens, distorted ted guitar, and agg reschve vocals, creating higheeerryy zy and intonne sound, row on energy, frenetk paco, and innovotive use of clectronic production techniques
synthwes technilguee,
The content bath tyfiss vacal idenity so unced from popujar brends, with extraction driven by relevant keywords and ashtage acress socia media planforme.
Figure 4: Distribution of AT-generated songs in our dataset.
A.3 Prompt Metadata and Style Attributes
Each generated song is associated with prompt metadata and stylistic attributes which provide a rich rep- resentation of musical intent. These structured tags help in conditioning models, analyzing generalization, and understanding detection robustness.
A.3.1 Genre List
pop, rock, hip-hop, R&B, EDM, jazz, blues, country, metal, punk, folk, classical, ambient, lo-fi, trap, drum and bass, dubstep, synthwave, vaporwave, house, techno, trance, reggae, dancehall, afrobeats, k-pop, j-pop, gospel, funk, soul, indie rock, indie pop, math rock, psychedelic, ezperimental, industrial, noise, electroa- coustic, bossa nova, samba, latin pop, grime, phonk; drill, hardstyle, gabber, post-rock, post-punk, new wave, retrowave, dream pop, shoegaze, dark ambient, minimal techno, chillwave, trip-kop, future bass, glitch hop, electropop, synth-pop,
soul, allernative metal, progressive metal, black metal, death metal, sludge metal, djent, folk metal, jazz fusion, smooth jazz, big band, bebop, cool jazz, bluegrass, americana, celtic, singer- songwriter, anime opening, game music, cinematic, soundtrack, orchestral, epic score, gregorian chant, chorale, opera, baroque, romantic era, modern classical, contemporary classical
A.3.2 Mood (Vibe) Tags
energetic, melancholic, uplifting, dark, dreamy, romantic, rebellious, introspective, nostalgic, aggressive, peaceful, mysterious, epic, groovy, funkyy, sultry, eerie, haunting, hypnotic, playful, somber, jowful, ambient, chill, moody, dramatic, whimsical, ethereal, gritty, row, emotional, spiritual, majestic, cinematic, futuristic, retro, vintage, spacey, glitchy, lo-fi, higg-energy, slow-burning, minimalist, mazimalist, experimental, ovant- garde, traditional, modern, classic, innovative, bold, subtle, intense, light-hearted, serene, chaotic, structured, free-form, rhythmic, melodic, percussive, harmonic, dissonant, consonant, layered, sporse.
A.3.3 Tempo Range
60 BPM, 65 BPM, 70 BPM, 75 BPM, 80 BPM, 85 BPM, BPM, 95 BPM, 100 BPM, 105 BPM, 110 BPM, 115 BPM, 120 BPM, 125 BPM, 130 BPM, 135 BPM, 140 BPM, 145 BPM, 150 BPM, 155 BPM, 160 BPM, 165 BPM, 170 BPM, 175 BPM, 180 BPM
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A.3.4 Key Signatures
C major, G major, D major, A major, E major, B major, F# major; C# major, F major, Bb major, Eb major, Ab major, D6 major, Gb major, Cb major, minor, minor, minor, F# minor, C# minor, G# minor, D# minor, A# minor, minor, minor, C minor, F minor, Bb minor, Eb minor, Ab minor
A.3.5 Focal Points
haunting female vocals, Spanglish verses, melodic rep, funky basslines, Afrobeats rhythmss Latin percus- sion, ambient syrth layers, dynamic drum patterns, side-chaining production, pop-drop structure, Afropiano elements, Arabic melodic inftuences, vintage 80s synths, orchestral string flourishes glitch effects, lo-fi tex- tures, trap hi-hats, 808 bass, guitar solos, piano arpeggios, sazophone riffs, violin sections, choir harmonies, turntable scratches, beatboring, synth arpeggios, modular synths, field recordings, spoken word segments, call-and-respore vocals
A.3.6 Extra Descriptors
a relro-futurislic almosphere, cinemalic Iransitions, subile glilchy lexlures, inlrospeclive lyrics, 808-style percussion, dreamlike vocal layering, lo-fi tertures, neon-lit arcade vibes, space-themed effects, underwater ambiance, forest soundscapes, urbon street noise, vintage radio samples, tope hiss, vinyl crackle, crowd chants, live concert feel, studio ambiance, rain sounds, wind effects, birdsong, ocean waves, city traffic, subway sounds, clock ticking, heartbeat rhythm, typewriter clicks, comera shulters
A.4 Prompt Examples of Fully Fake songs
Prompt Type A Prompts Inspired by Existing Songs with Genre Conditioning (10 samples)
Prompt: Create an English song inspired by the song 'On The Alamo', in the style Noise.
Prompt: Create an english song inspired by the song 'There'l be Some Changes Made', the style of Rap.
Prompt:
Create an english song inspired by the song 'ot Me Roll It Remastered 2010', in the style of Polka.
Create an english song inspired by the song 'No Mother In This World Today', in the style of Bltal-Motal.
Prompt:
Prompt: Create an English song inspired by the song FFly Out', in the style of Interview.
Prompt:
Create an English song inspired by the song II Want It All (feat. Mack 10)', in the style of South Indian Traditional.
Prompt: Create an English song inspired by the song 'You Are My Everything", in the style of Techno.
Prompt: Create an English song inspired by the song 'Once In Blue Moon', in the style of Poetry.
Prompt: Create an English song inspired by the song 'Somewhere', in the style of Musique Concrete.
Prompt: Create an English song inspired by the song 'It Gets Better', in the style of Goth.
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Published in Transactions on Machine Learning Research (11/2025)
Prompt Box Prompts Curated Using Musical Features and Attributes (10 samples)
Post-rock fusion: Uplifting yet somber G minor at 95 BPM. Piano arpeggios meet vintage synths and Afrobeats rhythms. Subway ambience weaves through cinematic transitions. Lyrical themes of urban loneliness.
Prompt:
Prompt: Craft a Latin Pop track: sparse yet modern. Tempo: 135 BPM, major. Instrumentation: glitchy beats, ambient synths, lo-fi textures. Mood: dreamy, cinematic. Lyrical themes: longing, nostalgia. Arrangement: cinematic transitions, layered vocals.
Prompt:
Trip-hop track; romantic yet high-energy. 80 BPM, G# minor. Vintage synths, ambient pads, guitar solo. Retro-futuristic feel with rain. Melancholy layered with driving rhythm; a dance the downpour. Lyrical themes: longing, passion.
Prompt: Craft an experimental track (romantic chaos) at 120 BPM in A major. Imagine saxophone cries amidst synth arpeggios, grounded by Latin percussion. Vinyl crackle dusts cinematic transi- tions. Lyrical themes touch upon both love and destruction.
Prompt: Craft a DnB track at 155 BPM in major, blending cinematic grandeur with avant garde grit. Think: Afropiano-infused pop-drop, lo-fi textures, and colossal 808 drums. Dreamlike, layered vocals float over the chaos, lyrically exploring [Lyrical themes].
Prompt:
Craft bold, gritty funk track (175 BPM, C Major). Haunting female vocals meet lo-fi textures and trap hi-hats. Dreamlike vocal layers interweave with typewriter clicks, adding a unique, unsettling ambiance. Lyrical themes: defiance, longing.
Prompt: Genre: Classical/ Fusion. Instrumentation: Orchestra, 808, vocals. Mood: Ethereal, energetic. Tempo: 175 BPM. Key: minor. Lyrical Themes: Oceanic exploration, freedom. Arrangement: Free-form classical structure with funky 808 bass, call-and-rosponse vocals, layered with ocean waves underwater ambiance.
Prompt: Craft neo-classical track; genre: Classical. Instrumentation: Violin sections, field recordings. Mood: Uplifting, layered. Tempo: 125 BPM. Key: C Major. Arrangement: Tape hiss, neon arcade vibes, creating an energetic yet nostalgic soundscape.
Craft rebellious yet groovy drum and bass track (175 BPM, E minor). Instrumentation: pounding drums, pulsating bass, piano arpeggios, and Afrobeats rhythms. Mood: introspective, rebellious. Lyrical themes: rebellion, rain, reflection. Arrangement: rain intro builds to an explosive, introspective lyrical drop.
Prompt:
Prompt: Craft a modern classical piece (80 BPM, A minor). Imagine joyful, harmonic melodies soaring over funky basslines, punctuated by Spanglish verses. Retro-futuristic synths and spacey effects create an otherworldly atmosphere.
A.5 Modality Gap Detection via Embedding Alignment
We propose novel audio detection model, CLAM (Contrastive Learning for Audio Matching), that employs dual pre-trained encoders:
MERT: Captures rich musical representations
Wave2Vec2: Captures vocal nuances
These are fused via weighted cross-aggregatiio module. The model is trained using hybrid objective:
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Published in Transactions on Machine Learning Research (11/2025)
Binary Classification Loss (BOEWithh to distinguish real vs. Al-generated samples
Triplct Margin Loss to contrastively align real instrumental and vocal emboddings, helping the model learn natural semantic coherence
To effectively distinguish between Al-generated and real music, aligning the instrumental and vocal embed- ding spaces for real music samples is crucial. This alignment ensures that the multimodal representations of real music exhibit consistent and prodictable relationship in the learned embedding space, which can then be leveraged by the classifier to identify deviations present in synthetic content. Unlike unimodal analysis, comparing the aligned multimodal embeddings allows the detection of subtle inconsistencies characteristic of generative processes.
Contrastiiv learning approaches are particularly well-suited for this task as they focus on learning metric embedding space where notion of similarity is encoded by distance. The objective is to learn a mapping Φ X' + gd such that for any two samples Tj their distance in the embedding space, D()(z;), o(x;)), reflects their semantic similarity. Among various contrastive methods, Triplet Loss was explored for aligning the instrumental and vocal embeddings of real music samples.
The core idea behind Triplet Loss is to enforce a relative distance constraint. For a given Anchor sample a, a Positive sample p (which semantically similar to a), and a Negative sample n (which semantically dissimilar to a), the loss minimizes the distance between the Anchor and the Positive (D(e (P(e)sep)) while simultancously maximizing the distance between the Anchor and the Negative (D(ea,e,)) This is done such that the distance to the positive is smaller than the distance to the negative by at least predefined margin a. For embeddings ea = o(a), ep = k(p), and en = Φ(n), the Triplet Loss Liriplet is defined as:
Ceriplet (Ca; Epp en) max D(e,; ep) - D(eas en.) +x)
where :) denotes distance metric, and is the margin. The squared Euclidean distance, ||e-eey is commonly used for D(e e,)² due to its computational efficiency and differentiability. The max(0,-) function, also known as the hinge loss, ensures that no penalty is incurred if the distance constraint is already satisfied.
The optimization objective of minimizing Liriplet encourages the model to learn an embedding function ø such that for any valid triplet (a,p, n), the following inequality holds in the embedding space:
llea - e2l + S llea - en
This inequality directly promotes structured embedding space where embeddings of similar items are clustered together, and clusters of dissimilar items are pushed apart by a significant margin.
In the context of aligning instrumental and vocal embeddings for real music samples within training batch, the triplet can be constructed using an in-batch mining strategy. For each real music sample in the batch, we define the triplet as follows:
Anchor (ea): The instrumental embedding of real music track i from the batch (L)eal())
Positive (ep): The vocal embedding of the same real music track i from the batch (Etea (i))-
Negative (ex): The vocal embedding from different real music track j within the same batch (E] where } 1).
The objective is to make the vocal embedding of the correct track closer to its corresponding instrumental embedding than any vocal embedding from other tracks present in the batch, by a margin a. This specific triplet. configuration directly enforces that the learned embedding space respects the natural pairing of instrumental and vocal streams within authentic music.
The process for calculating the in-batch Triplet Loss for alignment, considering all valid triplets formed by pairing each Anchor-Positive pair with all other available Negatives within the batch, is formally described
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Published in Transactions on Machine Learning Research (11/2025)
in Algorithm 1
This in-batch mining strategy is a practical approximation to using all possible triplets in the dataset and is effective in practice by providing a diverse set of negative examples during training.
Algorithm 1: In-Batch Triplet Loss Calculation for Alignment
Input: Batch of data containing instrumental embeddings E1, vocal embeddings Ev, and labels L.
Output: Average Triplet Loss for the real samples in the batch. Eycal + {e; E E₁ L; 0}
PV (ey By Log 0} N - |
Number of real samples in the batch
Cbatch +-0
count
if N > then
for i t 0 to N - do
Indices start from 0 in code ea + b- [t]
> Anchor: Instrumental embedding of real sample Ep Expal
b Positive: Vocal embedding of real sample
for j s- 0 to N -
do
if i + then
Gn Evall []]
> Negative: Vocal embedding of real sample j dous t llea Epl12
Squared L2 distance daag + llea - en
Leriplet 1j & max 0, of - - + a)
Lbatch Lbatch + Ltriplet 5
count <- count +
end end end end
if count then
Chatch t Lbatch/ count end return Liatch
Average loss over valid triplets
The overall training objective for CLAM utilizes a dual-loss formulation: the Binary Cross-Entropy (BCF) loss for the final real/fake classification and the alignment loss to structure the embedding space for real samples. Specifically, the total loss is weighted of the BCE loss (LACR) and the Triplet Loss (Luripter) caleulated described above:
Fiotal LBCE + X Leriplet
where À is the alignment loss_ loss_weight weight hyperparameter controlling the influence of the alignment objective. By minimizing this combined loss, the model learns to simultaneously classify the music's ' origin and structure the embedding space such that real multimodal pairs are distinguishable from other combinations.
In our ablation study comparing various alignment loss functions, including Mean Squared Error (MSE), LI Loss, Huber Loss, Cosine Similarity Loss, and Triplet Loss, using this dual-loss objective, the Triplet Loss achieved the best overall performance in terms of classification accuracy and F1-score on the validation set. This empirical finding supports the hypothesis that explicitly enforcing relative distance margin between positive (aligned real pairs) and negative (misaligned real pairs) in the embedding space is highly effective for learning discriminative representations for Al-generated music detection. The structured embedding space learned via Triplet Loss enhances the downstream classifier's ability to identify the subtle, unnatural discrepancies present in synthetic content.
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Published in Transactions on Machine Learning Research (11/2025)
B Statistical Significance
To validate that the observed performance improvements are not a result of random chance, we performed statistical siguificance lesting. We employed a two-sided McNemar's 's test, & standard non-parametric proce- dure for comparing the error rates two classifiers on the same held-out test set.
Test Procodure: The operates by analyzing the predictions of any two models being compared on a per-sample basis. For each pair of models, we construct x contingency table that tallies the number of test samples where:
(a) Both models were correct.
(b) Both models were incorrect.
(c) The first model was correct and the second was incorrect.
(d) The second model was correct and the first was incorrect.
The test's null hypothesis is that the two models have the error rate. It focuses the cells of disagreement, (c) and (d). If the difference in the counts of these two cells is large enough, reject the mull hypothesis and conclude that one model has a significantly lower error rate than the other. The test statistic is calculated from these disagreement counts, and a p-value is derived.
Results: We applied this test to our key comparisons on the MoM benchmark:
Our best-performing model, CLAM (Triplet Loss), shows a highly statistically significant im- provement over both the previous SOTA, SpecTTTra (p < 0.001) [0.0000767], and the strongest baseline, Poin-HierNet (p < 0.001) [0.0000319].
The addition of the alignment loss provides highly statistically significant improvement, shown by comparing CLAM (Triplet Loss) to CLAM (No Alignment Loss) (p < 0.001) [0.0000223].
These results provide strong statistical evidence that both our overall CLAM architecture and the specific contribution of the Triplet alignment loss lead to meaningful and reliable performance gains.
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Reviews:
[{"review_id": "BvH6V5X5XL", "reviewer": "Reviewer_WSc9", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a large scale and diverse dataset for detecting machine generated music, along with a detection model trained using a contrastive objective. The proposed model achieves superior performance on the MoM and SONICS datasets (noting that performance on SONICS is saturated, with a reasonable explanation provided).", "claims_and_evidence_yn": "Yes", "claims_explanation": "Overall, the paper's methodology seems to be well justified.", "audience_yn": "Yes", "audience_explanation": "Yes, both the dataset and the presented modelling approach and results appear to be of interest to the music AI community.", "requested_changes": "Modeling Component:\nIt would be valuable to explore the effect of using different frozen embedders, or at least provide a justification for the specific choices made, i.e., why these particular embedders were selected over other potential candidates.\n\nBenchmark Datasets:\nAlthough FSD, SingFake, and CtrSVDD are much smaller than SONICS, reporting performance on these datasets (for both SpecTTTra and CLAM) would be helpful to contextualise the work, given their diverse compositions and different nature.\n\nImbalance in Generated Material Across Models:\nThe synthetic portion of the dataset includes samples from the following models, but the distribution is noticeably imbalanced:\n\nSuno v2 – 110 samples\nSuno v3 – 3,512 samples (Test only)\nSuno v3.5 – 23,695 samples\nSuno v4 – 48 samples (Test only)\nUdio v1.5 – 19,500 samples\nDiffrythm – 4,606 samples\nRiffusion – 7,057 samples (Test only)\nYue – 5,278 samples (Test only)\nVoice Cloning – 1,166 samples (Test only)\n\nWhy does this imbalance exist? It is arguably important for assessing the quality of the dataset and identifying potential biases.\n\nUser Study:\nMore detailed information is needed regarding the study participants, specifically, the number of individuals involved, their backgrounds, and their levels of expertise. Additionally, musicality is a highly subjective metric (here I am stating the obvious). While this has been extensively debated in the field, it remains important to approach such evaluations with as much rigour and transparency as possible. Given the current high-level framing of the musicality question and the lack of detail about participant backgrounds and expertise, it is difficult to contextualise or interpret the musicality scores meaningfully.\n\nFurthermore, the A-B testing signal is not clearly gauged, particularly with respect to the optional voting on musicality (how often was this considered by users, given its optional nature?) and lyrics (when lyrics are present, were they consistently taken into account?). These uncertainties undermine the reliability of the A-B selection mechanism and the ELO scores, making it unclear what exactly it measures.", "broader_impact_concerns": "This paper reasonably reflects on the broader impacts of its work, given its focus on detecting machine-generated music.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "riIgH1E5iS", "reviewer": "Reviewer_EVaV", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a new synthetic music dataset comprising a diverse set of audio samples from a large collection of generative models, along with an out-of-distribution test set that reflects the real-world application challenges of deepfake detection methods.\nMoreover, the authors present an audio deepfake detection model that consists of two different pre-trained audio encoders (Wav2Vec2 & MERT), each extracting different musical nuances from the audio samples, and a triplet + BCE loss. The incorporation of two different audio encoders is novel. Applied to their own and other related synthetic audio datasets, the authors' model performs better on both in-distribution and out-of-distribution samples.", "claims_and_evidence_yn": "No", "claims_explanation": "The authors claim that even though deepfake detection methods perform well on some in-distribution synthetic audio datasets, they perform poorly on diverse out-of-distribution data that is most likely to be encountered in real-world applications. Their empirical evidence for this claim is shown in Tables 6 & 7. \n\nHowever, a thorough comparison against SOTA methods that learn strong audio representations and can be used (fine-tuned or out of the box) for deepfake detection is missing. Thus, stating SOTA performance should be more empirically validated.\n\nOther strong baselines could include: \n\n[1] Guo, Yinlin, et al. \"Audio deepfake detection with self-supervised wavlm and multi-fusion attentive classifier.\" ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.\n\n[2] Phukan, Orchid Chetia, et al. \"Heterogeneity over homogeneity: Investigating multilingual speech pre-trained models for detecting audio deepfake.\" arXiv preprint arXiv:2404.00809 (2024).\n\n[3] Serrà, Joan, et al. \"Supervised contrastive learning from weakly-labeled audio segments for musical version matching.\" arXiv preprint arXiv:2502.16936 (2025).", "audience_yn": "Yes", "audience_explanation": "The synthetic music generation and audio deepfake detection communities might be strongly interested in this.", "requested_changes": "- Please provide a comparison against more models that have been shown to learn strong audio representations and can be applied (fine-tuned or out of the box) to the task of audio deepfake detection.\n- Provide the formulation of the used triplet loss in more detail.\n- Ablation study, where the classical SimCLR [1] contrastive loss would be helpful to understand the benefit of the triplet loss in this scenario.\n\n\n\n[1] Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International conference on machine learning. PmLR, 2020.", "broader_impact_concerns": "All impact concerns are sufficiently addressed in the Broader Impact Statement sections of the paper.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "YBw1FS27KJ", "reviewer": "Reviewer_djYv", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "- This paper created a large dataset for AI music generation detection, MoM. The dataset includes the use of multiple generative AI Music models, but the models are partitioned into training and testing, meaning one model's generations cannot be in both the training and testing splits. The dataset also includes a more diverse set of samples when compared to previous datasets. The classes include Real, Fake, and Mostly Fake. The Mostly Fake category contains samples that are generated with human guidance, such as given lyrics or singer voice timbre. \n-  Additionally, this paper introduced a novel deepfake AI music detection model, CLAM. CLAM uses multiple models, MERT and Wave2Vec2, to encode the musicality and vocal details respectively. The use of the triplet loss increases the probability of real songs being identified correctly. \n- The results show that on previous datasets (Sonic), CLAM outperforms existing models (SpecTTTra) by a significant margin. When testing on MoM, every version of CLAM consistently outperforms previous models in accuracy and F1 score.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The submission goes into detail about the contents of previous AI music detection datasets and their flaws. Specifically, it shows the lack of diverse data samples, and then attempts to mitigate these problems. Also, previous datasets have far fewer samples, which MoM attempts to correct.\n\nCLAM explains each layer and the loss in detail, and provides extensive experimental design details.", "audience_yn": "Yes", "audience_explanation": "The problem this paper is attempting to improve at is a very important problem, especially with the rise of deepfake AI generation. This paper will promote further development into the area of AI music generation detection.", "requested_changes": "In section 2.2, the paper mentions several self-supervised audio encoders, but only evaluates MERT and Wave2Vec2. \n\nMultiple times throughout the paper, the authors are cited with no parantheses.", "broader_impact_concerns": "All ethical concerns are addressed in section 8.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)