Improving Machine Hearing on Limited Data Sets Convolutional neural network (CNN) architectures have originated and revolutionized machine learning for images. In order to take advantage of CNNs in predictive modeling with audio data, standard FFTbased signal processing methods are often applied to convert the raw audio waveforms into an imagelike representations (e.g. spectrograms). Even though conventional images and spectrograms differ in their feature properties, this kind of preprocessing reduces the amount of training data necessary for successful training. In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting. We compare the standard melspectrogram inputs with a newly proposed representation, called Mel scattering. Furthermore, we investigate the impact of additional target data representations by using an augmented target loss function which incorporates unused available information. We observe that all proposed methods outperform the standard meltransform representation when using a limited data set and discuss their strengths and limitations. The source code for reproducibility of our experiments as well as intermediate results and model checkpoints are available in an online repository.

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.