  This paper presents a successful application of deep learning for object
recognition based on acoustic data. The shortcomings of previously employed
approaches where handcrafted features describing the acoustic data are being
used, include limiting the capability of the found representation to be widely
applicable and facing the risk of capturing only insignificant characteristics
for a task. In contrast, there is no need to define the feature representation
format when using multilayer/deep learning architecture methods: features can
be learned from raw sensor data without defining discriminative characteristics
a-priori. In this paper, stacked denoising autoencoders are applied to train a
deep learning model. Knocking each object in our test set 120 times with a
marker pen to obtain the auditory data, thirty different objects were
successfully classified in our experiment and each object was knocked 120 times
by a marker pen to obtain the auditory data. By employing the proposed deep
learning framework, a high accuracy of 91.50% was achieved. A traditional
method using handcrafted features with a shallow classifier was taken as a
benchmark and the attained recognition rate was only 58.22%. Interestingly, a
recognition rate of 82.00% was achieved when using a shallow classifier with
raw acoustic data as input. In addition, we could show that the time taken to
classify one object using deep learning was far less (by a factor of more than
6) than utilizing the traditional method. It was also explored how different
model parameters in our deep architecture affect the recognition performance.
