  The primary objective of this paper is to build classification models and
strategies to identify breathing sound anomalies (wheeze, crackle) for
automated diagnosis of respiratory and pulmonary diseases. In this work we
propose a deep CNN-RNN model that classifies respiratory sounds based on
Mel-spectrograms. We also implement a patient specific model tuning strategy
that first screens respiratory patients and then builds patient specific
classification models using limited patient data for reliable anomaly
detection. Moreover, we devise a local log quantization strategy for model
weights to reduce the memory footprint for deployment in memory constrained
systems such as wearable devices. The proposed hybrid CNN-RNN model achieves a
score of 66.31% on four-class classification of breathing cycles for ICBHI'17
scientific challenge respiratory sound database. When the model is re-trained
with patient specific data, it produces a score of 71.81% for leave-one-out
validation. The proposed weight quantization technique achieves ~4X reduction
in total memory cost without loss of performance. The main contribution of the
paper is as follows: Firstly, the proposed model is able to achieve state of
the art score on the ICBHI'17 dataset. Secondly, deep learning models are shown
to successfully learn domain specific knowledge when pre-trained with breathing
data and produce significantly superior performance compared to generalized
models. Finally, local log quantization of trained weights is shown to be able
to reduce the memory requirement significantly. This type of patient-specific
re-training strategy can be very useful in developing reliable long-term
automated patient monitoring systems particularly in wearable healthcare
solutions.

Audio and Speech Processing;Machine Learning;Sound;Machine Learning