Sound event detection systems are widely used in various applications such as surveillance and environmental monitoring where data is automatically collected, processed, and sent to a cloud for sound recognition. However, this process may inadvertently reveal sensitive information about users or their surroundings, hence raising privacy concerns. In this study, we propose a novel adversarial training method for learning representations of audio recordings that effectively prevents the detection of speech activity from the latent features of the recordings. The proposed method trains a model to generate invariant latent representations of speech-containing audio recordings that cannot be distinguished from non-speech recordings by a speech classifier. The novelty of our work is in the optimization algorithm, where the speech classifier's weights are regularly replaced with the weights of classifiers trained in a supervised manner. This increases the discrimination power of the speech classifier constantly during the adversarial training, motivating the model to generate latent representations in which speech is not distinguishable, even using new speech classifiers trained outside the adversarial training loop. The proposed method is evaluated against a baseline approach with no privacy measures and a prior adversarial training method, demonstrating a significant reduction in privacy violations compared to the baseline approach. Additionally, we show that the prior adversarial method is practically ineffective for this purpose.

翻译：声音事件检测系统广泛应用于监控和环境监测等各种场景中，这些系统会自动收集、处理数据并将其发送至云端进行声音识别。然而，此过程可能会无意中泄露用户或其周围环境的敏感信息，从而引发隐私问题。在本研究中，我们提出了一种新颖的对抗性训练方法，用于学习音频录音的表示，该方法能有效防止从录音的潜在特征中检测出语音活动。所提出的方法训练一个模型，生成包含语音的音频录音的不变潜在表示，使得语音分类器无法将其与非语音录音区分开。我们工作的创新之处在于优化算法：在对抗性训练过程中，语音分类器的权重会定期替换为以监督方式训练的分类器权重。这持续增强了语音分类器的判别能力，从而促使模型生成语音不可区分的潜在表示——即使使用在对抗性训练循环外新训练的语音分类器也无法区分。我们将所提方法与无隐私措施的基线方法及先前的对抗性训练方法进行对比评估，结果表明该方法相较于基线方法显著减少了隐私泄露。此外，我们还证明先前的对抗性方法在此任务中实际上无效。