Compromising Speech Privacy under Continuous Masking in Personal Spaces This paper explores the effectiveness of common sound masking solutions deployed for preserving speech privacy in workplace environment such as hospitals, financial institutions, lawyers offices, nursing homes and government buildings. With the increased awareness about personal privacy among the general population, we set out to examine the effectiveness of current speech privacy preserving tools. We seek to determine if the general approach used by the current masking mechanisms is adequate to provide the level of privacy desired from these solutions. In addition, we also seek to investigate preservation of speech privacy in the face of ubiquitous and less conspicuous devices like smartphones that possess the capability of sound recording with inbuilt noise cancellation technology. Our approach in this paper is to expose the vulnerability in sound masking technology in scenarios that require preserving privacy in personal spaces. We use human listeners to attack speech privacy under sound masking where we aim to identify spoken words eavesdropped under different scenarios. We also test currently available speech recognition tools to assess their performance at decoding speech in noisy environment. Our results indicate that pink noise, the commonly used technology to provide speech privacy for use in personal space, is ineffective against a dedicated eavesdropping adversary that uses commonplace devices such as smartphones to record the speech and noise reduction tools to counteract sound masking.

AudioMask: Robust Sound Event Detection Using Mask RCNN and FrameLevel Classifier Deep learning methods have recently made significant contributions to sound event detection. These methods either use a blocklevel approach to distinguish parts of audio containing the event, or analyze the small frames of the audio separately. In this paper, we introduce a new method, AudioMask, for rare sound event detection by combining these two approaches. AudioMask first applies Mask RCNN, a stateoftheart algorithm for detecting objects in images, to the log melspectrogram of the audio files. Mask RCNN detects audio segments that might contain the target event by generating bounding boxes around them in timefrequency domain. Then we use a framebased audio event classifier trained independently from Mask RCNN, to analyze each individual frame in the candidate segments proposed by Mask RCNN. A postprocessing step combines the outputs of the Mask RCNN and the framelevel classifier to identify the true events. By evaluating AudioMask over the data sets from 2017 Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge Task 2, We show that our algorithm performs better than the baseline models by 13.3% in the average Fscore and achieves better results compared to the other nonensemble methods in the challenge.