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类场景，在这些场景中，数据被自动收集、处理并发送至云端进行声音识别。然而，这一过程可能无意中泄露用户或其周围环境的敏感信息，从而引发隐私担忧。在本研究中，我们提出了一种新颖的对抗训练方法，用于学习音频记录的表示，从而有效防止从记录的潜在特征中检测到语音活动。所提出的方法训练一个模型，生成包含语音的音频记录的不变潜在表示，使得语音分类器无法将这些表示与不含语音的记录区分开来。我们工作的创新之处在于优化算法：在对抗训练过程中，定期用监督训练的分类器权重替换语音分类器的权重。这持续提升了语音分类器的判别能力，激励模型生成语音无法区分的潜在表示，即使使用对抗训练循环之外新训练的语音分类器也无法区分。该方法与无隐私保护措施的基线方法及先前的对抗训练方法进行了对比评估，结果显示，与基线方法相比，隐私侵犯行为显著减少。此外，我们还表明，先前的对抗方法在实际中对此目的无效。