Cure Dataset: Ladder Networks for Audio Event Classification Audio event classification is an important task for several applications such as surveillance, audio, video and multimedia retrieval etc. There are approximately 340 million people with hearing loss who canxe2x80x99t perceive events happening around them. This paper establishes the CURE dataset which contains curated set of specific audio events most relevant for people with hearing loss. It is formatted as 5 sec sound recordings derived from the Freesound project. We propose a ladder network based audio event classifier. We adopted the stateoftheart convolutional neural network (CNN) embeddings as audio features for this task. We start with signal and feature normalization that aims to reduce the mismatch between different recordings scenarios. Initially, a CNN is trained on weakly labeled Audioset data. Next, the pretrained model is adopted as feature extractor for proposed CURE corpus. We also explore the performance of extreme learning machine (ELM) and use support vector machine (SVM) as baseline classifier. As a second evaluation set we incorporate ESC50. Results and discussions validate the superiority of Ladder network over ELM and SVM classifier in terms of robustness and increased classification accuracy.

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.