Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2410.09778

LEAD Dataset: How Can Labels for Sound Event Detection Vary Depending on Annotators?

Naoki Koga·Yoshiaki Bando·Keisuke Imoto
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, we introduce a LargE-scale Annotator's labels for sound event Detection (LEAD) dataset, which is the dataset used to gain a better understanding of the variation in strong labels in sound event detection (SED). In SED, it is very time-consuming to collect large-scale strong labels, and in most cases, multiple workers divide up the annotations to create a single dataset. In general, strong labels created by multiple annotators have large variations in the type of sound events and temporal onset/offset. Through the annotations of multiple workers, uniquely determining the strong label is quite difficult because the dataset contains sounds that can be mistaken for similar classes and sounds whose temporal onset/offset is difficult to distinguish. If the strong labels of SED vary greatly depending on the annotator, the SED model trained on a dataset created by multiple annotators will be biased. Moreover, if annotators differ between training and evaluation data, there is a risk that the model cannot be evaluated correctly. To investigate the variation in strong labels, we release the LEAD dataset, which provides distinct strong labels for each clip annotated by 20 different annotators. The LEAD dataset allows us to investigate how strong labels vary from annotator to annotator and consider SED models that are robust to the variation of strong labels. The LEAD dataset consists of strong labels assigned to sound clips from TUT Sound Events 2016/2017, TUT Acoustic Scenes 2016, and URBAN-SED. We also analyze variations in the strong labels in the LEAD dataset and provide insights into the variations.

Explore related subjects

cs.SDeess.AS

Keep this discovery

BibTeXRIS

Naoki Koga, Yoshiaki Bando, Keisuke Imoto. 2024-10-13. LEAD Dataset: How Can Labels for Sound Event Detection Vary Depending on Annotators?. https://arxiv.org/abs/2410.09778

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD↗
arXiv2026

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD↗