Environmental sound segmentation utilizing Mask UNet This paper proposes an environmental sound segmentation method using Mask UNet. Recent research in robot audition has analyzed noise reduction, section detection, and sound source separation for use in a realworld environment with many noises and overlaps. However, conventional methods apply respective functions in cascades. The biggest problem of cascade systems is the accumulation of errors generated at each function block. Although many methods of human voice separation have been proposed, robots operating in a realworld environment must be able to separate not only human voices but other environmental sounds. Unlike traditional sound source separation using spatial information, environmental sound segmentation must simultaneously detect sections and separate sound sources based on pretrained features. One such method, UNet, which was proposed for semantic segmentation of images, has been applied to the separation of singing voices. However, this method deals only with limited classes of sounds. The current study proposes an environmental sound segmentation method using Mask UNet, which combines segmentation using UNet with sound event detection using CNN to 75classes of environmental sounds. Experimental application confirmed that this method improved learning speed and sound source separation compared with the conventional method.

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.