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.

The quality of infant-directed speech (IDS) and infant-directed singing (IDSi) are considered vital to children, but empirical studies on protomusical qualities of the IDSi influencing infant development are rare. The current prospective study examines the role of IDSi acoustic features, such as pitch variability, shape and movement, and vocal amplitude vibration, timbre, and resonance, in associating with infant sensorimotor, language, and socioemotional development at six and 18 months. The sample consists of 236 Palestinian mothers from Gaza Strip singing to their six-month-olds a song by their own choice. Maternal IDSi was recorded and analyzed by the OpenSMILE- tool to depict main acoustic features of pitch frequencies, variations, and contours, vocal intensity, resonance formants, and power. The results are based on completed 219 maternal IDSi. Mothers reported about their infants’ sensorimotor, language-vocalization, and socioemotional skills at six months, and psychologists tested these skills by Bayley Scales for Infant Development at 18 months. Results show that maternal IDSi characterized by wide pitch variability and rich and high vocal amplitude and vibration were associated with infants’ optimal sensorimotor, language vocalization, and socioemotional skills at six months, and rich and high vocal amplitude and vibration predicted these optimal developmental skills also at 18 months. High resonance and rhythmicity formants were associated with optimal language and vocalization skills at six months. To conclude, the IDSi is considered important in enhancing newborn and risk infants’ wellbeing, and the current findings argue that favorable acoustic singing qualities are crucial for optimal multidomain development across infancy.