UltraLowPower Intelligent Acoustic Sensing using CochleaInspired Feature Extraction and DNN Classification We present our recent progress in ultralowpower intelligent acoustic sensing that harnesses the high power and energy efficiency of cochlealike analog feature extraction and binarized neural network classification. Compared with conventional methods including the fast Fourier transformbased feature extraction plus neural network classification, and the more aggressive approach based on endtoend neural network models, the analog filter bankbased handcrafted feature extraction inspired by mammalian cochlea has the promise of achieving the minimum power consumption for many existing and emerging alwayson audio applications. System considerations and circuit techniques that are used to achieve the high power efficiency will be presented and comparison with some stateoftheart works, and future directions will be discussed.

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.