Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods

Xuanru Zhou; Yiwen Shao; Wei-Cheng Tseng; Dong Yu

cs.SDMar 26, 2026

Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods

Authors: Xuanru Zhou, Yiwen Shao, Wei-Cheng Tseng, Dong Yu

Organizations: UT-Austin, Austin, USA · Tencent AI Lab, Bellevue, USA

Abstract

Current audio pre-training seeks to learn unified representations for broad audio understanding tasks, but it remains fragmented and is fundamentally bottlenecked by its reliance on weak, noisy, and scale-limited labels. Drawing lessons from vision's foundational pre-training blueprint, we argue that the audio field must first establish its own large-scale, strong supervision framework. We introduce a new data-centric pipeline that leverages a high-fidelity captioner to create SOTA-quality captions and the first Unified Tag System (UTS) that bridges speech, music, and environmental sounds. We then conduct a systematic comparative study of different pre-training objectives on these strong source data. Our experiments suggest that data quality and coverage are the primary drivers of performance, while the choice of objective dictates downstream task specialization.

Figures & tables

Figure 1: An overview of our method. (a) Audio Tagging Pipeline: we generate tags by processing raw audio through Qwen3-Omni-Captioner [ 87 ] and an LLM Parser [ 90 ] . (b) Pre-Training Pipeline: the audio encoder is then pre-trained using these tags with two supervision objectives: a discriminative multi-tag classification (MTC) Objective (using a linear classifier) and a generative parellel decoding (PAR) Objective (using a text decoder with a bidirectional mask).

Panel of Figure 2 — Figure 2: Analysis of our tag system. (a) Word cloud of the most frequent tags in our parsed tags, illustrating its diverse vocabulary. (b) t-SNE comparison of our UTS-1.5k (blue) and AudioSet (red), demonstrating our system’s superior semantic coverage and density. (c) The characteristic long-tail tag frequency distribution of all parsed tags.

Dataset	Audio Coverage	Tag Size	Data Size
AudioSet [ 29 ]	General events (incl. speech, music)	527	$\sim$ 2M
FSD-50k [ 27 ]	General sound events	200	$\sim$ 50k
VggSound [ 9 ]	General sound events	309	$\sim$ 200k
CREMA-D [ 8 ]	Speech	6	$\sim$ 7k
MagnaTagATune [ 82 ]	Music	50	$\sim$ 24k
Ours	Unified (Speech, Music, Env. Sounds)	800/1k/1.5k/2k/3k	$\sim$ 400k

Table 1: Comparison of existing audio tagging datasets.

Methods	Label	Linear Probing							Audio-language Alignment
									Captioning			Retrieval
		FSD-50k	VggSound	AS-Strong	VoxCeleb2	CREMA-D	MTAT	NSynth	AC	PSC	MC	AC	PSC	MC
SSL Models
BEATs [ 12 ]	-	0.565 †	-	0.034 †	-	-	0.400 †	75.90 †	-	-	-	-	-	-
Wav2vec 2.0 [ 3 ]	-	0.342 †	-	-	51.60	56.10	0.317 †	40.20 †	-	-	-	-	-	-
MERT [ 51 ]	-	-	-	-	-	-	0.402 †	72.60 †	-	-	-	-	-	-

Table 2: Main results on linear probing (with mean-pooling) and audio-language alignment. The linear probing tasks span general audio (FSD-50k [ 27 ] , VggSound [ 9 ] , AudioSet-Strong [ 37 ] ), speech (VoxCeleb2 [ 15 ] , CREMA-D [ 8 ] ), and music (MagnaTagATune [ 82 ] , NSynth [ 25 ] ). See Supplementary Material Sec. 8 for full details of evaluation datasets and metrics. For audio-language alignment, AC, PSC, and MC refer to the AudioCaps [ 44 ] , ParaSpeechCaps [ 21 ] , and MusicCaps [ 1 ] , respectively. We bold the best score among our proposed models. Baseline scores that are underlined are surpassed by their corresponding “Ours” counterpart.

\dagger

denotes scores quoted from prior work with a consistent evaluation setup.

Methods	Open-formed QA
Methods	Sound	Speech (emotion/gender/age)	Music
Baselines
MTC (AudioSet)	7.01	47.16/ 25.90 / 47.27 / 45.06 / 37.24	5.61
Contrastive-scratch [ 78 ]	5.69	29.61 /48.79/ 55.25 /84.85/86.59	5.99
Captioning-scratch [ 78 ]	6.25	22.93 /37.40/70.20/78.93/40.14	5.73
Our Tag-Oriented Pre-Trained Models

Table 3: Open-formed Question Answering (QA) Results on the AIR-Bench [ 91 ] . The Sound and Music columns report 10-point scores from open-ended questions, evaluated by GPT-4o [ 62 ] . The five scores in the Speech column correspond to the classification accuracy on the Emotion-MELD, Emotion-IEMOCAP, Gender-MELD, Gender-common, and Age tasks, respectively.

Dataset	#audio/#cap	Domain	Audio source	Caption style	Caption generation pipeline
AudioCaps	46k/46k	general (environmental, human/animal sounds)	AudioSet	Human-annotated, short description	crowdsourced
Clotho	5k/25k	environmental sounds	FreeSound	Human-annotated, short description	crowdsourced
MusicCaps	3k/3k	music	AudioSet	Expert musician-written, multi-sentence, fine-grained description	expert curation
WavCaps	400k/400k	general (environmental, human/animal sounds)	AudioSet BBC Sound Effect FreeSound SoundBible	LLM-refined captions	three-stage pipeline: web-crawled raw descriptions $\rightarrow$ ChatGPT rewrite $\rightarrow$ filtering
AudioSetCaps	1.9M/1.9M 4.0M/4.0M 182k/182k	general (environmental, human/animal sounds)	AudioSet YouTube8M VggSound	LLM-generated, detailed, multi-sentence description	three-stage pipeline: LALM attribute extraction $\rightarrow$ LLM captioning $\rightarrow$ CLAP-based filtering
FusionAudio	1.2M/1.2M	general (environmental, human/animal sounds)	AudioSet	LLM-augmented, multi-sentence, visual-enhanced description	multimodal context fusion (audio, visual, metadata) + LLM captioning

Table 5: Overview of the public datasets constituting CaptionStew. The table summarizes their scale, domain coverage, audio sources, and diverse captioning pipelines (from human annotation to LLM generation).

Evaluation Dataset	Task	#samples	#class	train	eval	Metrics
FSD-50k	Multi-label audio event classification	37,168 / 10,231	200	$\checkmark$	$\checkmark$	mAP
VggSound	Single-label audio event classification	183,730 / 15,446	309	$\checkmark$	$\checkmark$	accuracy
VoxCeleb2	Speaker identification	1,092,009 / 36,693	5,994	$\checkmark$	$\checkmark$	accuracy
CREMA-D	Speech emotion recognition	6,030 / 706	6	$\checkmark$	$\checkmark$	accuracy
MagnaTagATune	Music tagging	19,425 / 4,856	50	$\checkmark$	$\checkmark$	mAP
NSynth	Musical instrument classification	289,205 / 4,096	11	$\checkmark$	$\checkmark$	accuracy

Table 6: Overview of the evaluation datasets used for assessing audio representation quality. † Evaluated by GPT-4o following the AIR-Bench protocol. ‡ Synthesized from publicly available speech datasets [ 2 , 7 , 8 , 54 , 63 ] using fixed question templates.

Methods	Label	Linear Probing							Audio-language Alignment
									Captioning			Retrieval
		FSD-50k	VggSound	AS-Strong	VoxCeleb2	CREMA-D	MTAT	NSynth	AC	PSC	MC	AC	PSC	MC
SSL Models
BEATs [ 12 ]	-	0.565 †	-	0.034 †	-	-	0.400 †	75.90 †	-	-	-	-	-	-
Wav2vec 2.0 [ 3 ]	-	0.342 †	-	-	51.60	56.10	0.317 †	40.20 †	-	-	-	-	-	-
MERT [ 51 ]	-	-	-	-	-	-	0.402 †	72.60 †	-	-	-	-	-	-

Table 7: Complete results on linear probing (with mean-pooling) and audio-language alignment. The linear probing tasks span general audio (FSD-50k [ 27 ] , VggSound [ 9 ] , AudioSet-Strong [ 37 ] ), speech (VoxCeleb2 [ 15 ] , CREMA-D [ 8 ] ), and music (MagnaTagATune [ 82 ] , NSynth [ 25 ] ). For audio-language alignment, AC, PSC, and MC refer to the AudioCaps [ 44 ] , ParaSpeechCaps [ 21 ] , and MusicCaps [ 1 ] , respectively. We bold the best score in our tag-oriented models and audio-language models respectively. Baseline scores that are underlined are surpassed by their corresponding “Ours” counterpart.

\dagger

denotes scores quoted from prior work with a consistent evaluation setup.

Methods	Linear Probing						Open-formed QA
Methods	FSD-50k	VggSound	VoxCeleb2	CREMA-D	MTAT	NSynth	Sound	Speech (emotion/gender/age)	Music
Baselines
MTC (AudioSet)	0.656	56.23	58.76	72.52	0.405	74.80	7.01	47.16/ 25.90 / 47.27 / 45.06 / 37.24	5.61
Contrastive-scratch [ 78 ]	0.534	46.79	70.18	69.97	0.385	70.19	5.69	29.61 /48.79/ 55.25 /84.85/86.59	5.99
Captioning-scratch [ 78 ]	0.483	43.43	44.51	66.29	0.382	68.31	6.25	22.93 /37.40/70.20/78.93/40.14	5.73
Our Tag-Oriented Pre-Trained Models

Table 8: Complete results on linear probing (with multi-head attention pooling) and open-formed QA. The Sound and Music columns report 10-point scores on open-ended questions from AIR-Bench, evaluated by GPT-4o [ 62 ] . The five scores in the Speech column correspond to the classification accuracy on the Emotion-MELD, Emotion-IEMOCAP, Gender-MELD, Gender-common, and Age tasks, respectively.

Audio Representation Learning

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Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored. Crucially, there is no consensus on whether audio-language models can build effective general-purpose audio encoders, nor a systematic understanding of how pretraining objectives behave across diverse tasks and scales. We identify three key barriers: limited scale of audio-text corpora, limited coverage of audio attributes in existing caption corpora, and lack of systematic exploration and evaluation. To fill this gap, we present the first principled empirical study of ALP. We first introduce CaptionStew, a 10.7M caption dataset aggregating open-source audio-text corpora across multiple domains and captioning focuses. We then conduct the first comprehensive evaluation comparing contrastive and captioning objectives for learning audio representation across speech, music, and environmental sound tasks. Our results not only demonstrate that ALP yields competitive, transferable representations, but reveal critical trade-offs: contrastive learning offers superior data efficiency, while captioning exhibits better scalability. Furthermore, we find that the benefits of supervised initialization often diminish at larger scales, challenging common practices. By grounding these claims in empirical evidence, we establish a viable pathway toward general-purpose audio representation learning, guiding future research.

Abstract

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