  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.
