Disrupting Audio Event Detection Deep Neural Networks with White Noise

Audio event detection (AED) systems can leverage the power of specialized algorithms for detecting the presence of a specific sound of interest within audio captured from the environment. More recent approaches rely on deep learning algorithms, such as convolutional neural networks and convolutional recurrent neural networks. Given these conditions, it is important to assess how vulnerable these systems can be to attacks. As such, we develop AED-suited convolutional neural networks and convolutional recurrent neural networks, and attack them next with white noise disturbances, conceived to be simple and straightforward to be implemented and employed, even by non-tech savvy attackers. We develop this work under a safety-oriented scenario (AED systems for safety-related sounds, such as gunshots), and we show that an attacker can use such disturbances to avoid detection by up to 100 percent success. Prior work has shown that attackers can mislead image classification tasks; however, this work focuses on attacks against AED systems by tampering with their audio rather than image components. This work brings awareness to the designers and manufacturers of AED systems, as these solutions are vulnerable, yet may be trusted by individuals and families.

Ambient Sound Recognition of Daily Events by Means of Convolutional Neural Networks and Fuzzy Temporal Restrictions

The use of multimodal sensors to describe activities of daily living in a noninvasive way is a promising research field in continuous development. In this work, we propose the use of ambient audio sensors to recognise events which are generated from the activities of daily living carried out by the inhabitants of a home. An edge–fog computing approach is proposed to integrate the recognition of audio events with smart boards where the data are collected. To this end, we compiled a balanced dataset which was collected and labelled in controlled conditions. A spectral representation of sounds was computed using convolutional network inputs to recognise ambient sounds with encouraging results. Next, fuzzy processing of audio event streams was included in the IoT boards by means of temporal restrictions defined by protoforms to filter the raw audio event recognition, which are key in removing false positives in real-time event recognition.

A Corpus-Based Evaluation of Beamforming Techniques and Phase-Based Frequency Masking

Beamforming is a type of audio array processing techniques used for interference reduction, sound source localization, and as pre-processing stage for audio event classification and speaker identification. The auditory scene analysis community can benefit from a systemic evaluation and comparison between different beamforming techniques. In this paper, five popular beamforming techniques are evaluated in two different acoustic environments, while varying the number of microphones, the number of interferences, and the direction-of-arrival error, by using the Acoustic Interactions for Robot Audition (AIRA) corpus and a common software framework. Additionally, a highly efficient phase-based frequency masking beamformer is also evaluated, which is shown to outperform all five techniques. Both the evaluation corpus and the beamforming implementations are freely available and provided for experiment repeatability and transparency. Raw results are also provided as a complement to this work to the reader, to facilitate an informed decision of which technique to use. Finally, the insights and tendencies observed from the evaluation results are presented.