Wheezing Sound Separation Based on Constrained Non-Negative Matrix Factorization

Abstract

Auscultation remains the first clinical examination that a physician performs to detect respiratory diseases originated by wheezes, which are the most specific asthmatic symptoms. It is common that respiratory sounds (normal breath sounds) acoustically interfere wheezes with both frequency and time domain. As a result, the physician's cognitive ability is reduced causing a misdiagnosis or inability to clearly hear all significant sounds to detect a pulmonary disease. This paper presents a constrained non-negative matrix factorization (NMF) approach to separate wheezes from respiratory sounds applied to single-channel mixtures. The proposed constraints, smoothness and sparseness, attempts to model common spectral behaviour shown by wheezes and normal breath sounds. Specifically, the spectrogram of a wheeze can be modelled as a narrowband spectrum (sparseness in frequency). However, the spectrogram of a normal breath sound can be modelled as a wideband spectrum (smoothness in frequency) with a slow temporal variation (smoothness in time). Experimental results report that the proposed method improves the audio quality of the wheezes removing most of the respiratory sounds, being a novel way to successfully apply a NMF approach to a wheeze/respiratory sound separation.