Machine Learning Based Dynamic Band Selection for Splitting Auditory Signals to Reduce Inner Ear Hearing Losses

Quality of hearing has been severely impacted due to signal losses occurs in the human inner ear explicitly in the region of cochlea. Loudness recruitment, degraded frequency selectivity and auditory masking are the major outward effects of inner ear hearing losses. Splitting auditory signals into frequency bands and presenting dichotically to both ears became a comprehensive solution to reduce inner ear hearing losses. However, these methods divide input signal into the fix number of frequency bands, this limits their applicability where signals have large variations in their spectral characteristics. To address this challenge, we have proposed machine learning based intelligent band selection algorithm to split auditory signals dynamically. Proposed algorithm analyze input speech signal based on spectral characteristics to determine the optimum number of bands required to effectively present major acoustic cues of the signal. Further, dynamic splitting algorithm efficiently divides signal for dichotic presentation. Proposed method has been examined on large number of subjects from different age groups and gender having cochlear hearing impairment. Qualitative and quantitative assessment shown significant improvement in the recognition score with substantial reduction in the response time

Modeling the Comb Filter Effect and Interaural Coherence for Binaural Source Separation

Typical methods for binaural source separation consider only the direct sound as the target signal in a mixture. However, in most scenarios, this assumption limits the source separation performance. It is well known that the early reflections interact with the direct sound, producing acoustic effects at the listening position, e.g. the so-called comb filter effect. In this article, we propose a novel source separation model, that utilizes both the direct sound and the first early reflection information to model the comb filter effect. This is done by observing the interaural phase difference obtained from the timefrequency representation of binaural mixtures. Furthermore, a method is proposed to model the interaural coherence of the signals. Including information related to the sound multipath propagation, the performance of the proposed separation method is improved with respect to the baselines that did not use such information, as illustrated by using binaural recordings made in four rooms, having different sizes and reverberation times