Formula-Supervised Sound Event Detection: Pre-Training Without Real Data

• URL: http://arxiv.org/abs/2504.04428v1
• Date: Sun, 06 Apr 2025 09:47:26 GMT
• Title: Formula-Supervised Sound Event Detection: Pre-Training Without Real Data
• Authors: Yuto Shibata, Keitaro Tanaka, Yoshiaki Bando, Keisuke Imoto, Hirokatsu Kataoka, Yoshimitsu Aoki,
• Abstract summary: We propose a novel formula-driven supervised learning (F) framework for pre-training an environmental sound analysis model.<n>Specifically, we outline detailed procedures and evaluate their effectiveness for sound event detection (SED)<n>We demonstrate that large-scale pre-training with Formula-SED significantly enhances model accuracy and accelerates training.
• Score: 18.860251552914367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel formula-driven supervised learning (FDSL) framework for pre-training an environmental sound analysis model by leveraging acoustic signals parametrically synthesized through formula-driven methods. Specifically, we outline detailed procedures and evaluate their effectiveness for sound event detection (SED). The SED task, which involves estimating the types and timings of sound events, is particularly challenged by the difficulty of acquiring a sufficient quantity of accurately labeled training data. Moreover, it is well known that manually annotated labels often contain noises and are significantly influenced by the subjective judgment of annotators. To address these challenges, we propose a novel pre-training method that utilizes a synthetic dataset, Formula-SED, where acoustic data are generated solely based on mathematical formulas. The proposed method enables large-scale pre-training by using the synthesis parameters applied at each time step as ground truth labels, thereby eliminating label noise and bias. We demonstrate that large-scale pre-training with Formula-SED significantly enhances model accuracy and accelerates training, as evidenced by our results in the DESED dataset used for DCASE2023 Challenge Task 4. The project page is at https://yutoshibata07.github.io/Formula-SED/

Related papers

• Early Stopping Against Label Noise Without Validation Data [54.27621957395026]
  We propose a novel early stopping method called Label Wave, which does not require validation data for selecting the desired model. We show both the effectiveness of the Label Wave method across various settings and its capability to enhance the performance of existing methods for learning with noisy labels.
  arXiv  Detail & Related papers  (2025-02-11T13:40:15Z)
• Foster Adaptivity and Balance in Learning with Noisy Labels [26.309508654960354]
  We propose a novel approach named textbfSED to deal with label noise in a textbfSelf-adaptivtextbfE and class-balancetextbfD manner. A mean-teacher model is then employed to correct labels of noisy samples. We additionally propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples.
  arXiv  Detail & Related papers  (2024-07-03T03:10:24Z)
• Impact of Noisy Supervision in Foundation Model Learning [91.56591923244943]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets.<n>We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Combating Label Noise With A General Surrogate Model For Sample Selection [77.45468386115306]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [91.15120211190519]
  This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks. We propose a light-weight black-box tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise.
  arXiv  Detail & Related papers  (2023-09-29T06:18:15Z)
• DiffSED: Sound Event Detection with Denoising Diffusion [70.18051526555512]
  We reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process. During training, our model learns to reverse the noising process by converting noisy latent queries to the groundtruth versions.
  arXiv  Detail & Related papers  (2023-08-14T17:29:41Z)
• Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation [16.283722126438125]
  Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples. Such curriculum is sub-optimal since it does not consider the actual label noise rate in the training set. This paper addresses this issue with a new noise-rate estimation method that is easily integrated with most state-of-the-art (SOTA) LNL methods.
  arXiv  Detail & Related papers  (2023-05-31T01:46:14Z)
• Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features [43.41573458276422]
  We introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network. The proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation.
  arXiv  Detail & Related papers  (2022-12-19T09:39:30Z)
• Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile [78.1212767880785]
  meta-learner is prone to overfitting since there are only a few available samples. When handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise. We present Eigen-Reptile (ER) that updates the meta- parameters with the main direction of historical task-specific parameters.
  arXiv  Detail & Related papers  (2022-06-04T08:48:02Z)
• Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models [13.456066434598155]
  We address the problem of detecting speech directed to a device that does not contain a specific wake-word. Specifically, we focus on audio coming from a touch-based invocation.
  arXiv  Detail & Related papers  (2022-03-30T01:27:39Z)
• Active Learning for Sound Event Detection [18.750572243562576]
  This paper proposes an active learning system for sound event detection (SED) It aims at maximizing the accuracy of a learned SED model with limited annotation effort. Remarkably, the required annotation effort can be greatly reduced on the dataset where target sound events are rare.
  arXiv  Detail & Related papers  (2020-02-12T14:46:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.