# The Benefit Of Temporally-Strong Labels In Audio Event Classification

arXiv: https://arxiv.org/abs/2105.07031 · HTML: https://mlsanity.com/paper/2105.07031/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2021.

## Built on by (most cited first, 30 of 83)

- [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024)
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- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities](https://mlsanity.com/paper/2503.03983/) (2025)
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- [Joint Audio and Speech Understanding](https://mlsanity.com/paper/2309.14405/) (2023)
- [MAViL: Masked Audio-Video Learners](https://mlsanity.com/paper/2212.08071/) (2022)
- [AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models](https://mlsanity.com/paper/2410.18325/) (2024)
- [Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation](https://mlsanity.com/paper/2401.01044/) (2024)
- [A-JEPA: Joint-Embedding Predictive Architecture Can Listen](https://mlsanity.com/paper/2311.15830/) (2023)
- [Epic-Sounds: A Large-scale Dataset of Actions That Sound](https://mlsanity.com/paper/2302.00646/) (2023)
- [CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models](https://mlsanity.com/paper/2310.08753/) (2023)
- [ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing](https://mlsanity.com/paper/2506.21448/) (2025)
- [EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer](https://mlsanity.com/paper/2409.10819/) (2024)
- [Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data](https://mlsanity.com/paper/2112.07891/) (2021)
- [AudioX: A Unified Framework for Anything-to-Audio Generation](https://mlsanity.com/paper/2503.10522/) (2025)
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- [FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation](https://mlsanity.com/paper/2507.08557/) (2025)
- [Audio Generation with Multiple Conditional Diffusion Model](https://mlsanity.com/paper/2308.11940/) (2023)
- [MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows](https://mlsanity.com/paper/2508.06098/) (2025)
- [Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning](https://mlsanity.com/paper/2512.19687/) (2025)
- [Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing](https://mlsanity.com/paper/2604.10708/) (2026)
- [DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning](https://mlsanity.com/paper/2410.09472/) (2024)
- [ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling](https://mlsanity.com/paper/2510.08878/) (2025)
- [You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection](https://mlsanity.com/paper/2109.00962/) (2021)
- [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023)
- [Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models](https://mlsanity.com/paper/2409.10999/) (2024)
- [A Whisper transformer for audio captioning trained with synthetic captions and transfer learning](https://mlsanity.com/paper/2305.09690/) (2023)

## Abstract (arXiv)

To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (~0.1 sec resolution) "strong" labels for a portion of the AudioSet dataset. We devised a temporally strong evaluation set (including explicit negatives of varying difficulty) and a small strong-labeled training subset of 67k clips (compared to the original dataset's 1.8M clips labeled at 10 sec resolution). We show that fine-tuning with a mix of weak and strongly labeled data can substantially improve classifier performance, even when evaluated using only the original weak labels. For a ResNet50 architecture, d' on the strong evaluation data including explicit negatives improves from 1.13 to 1.41. The new labels are available as an update to AudioSet.