Cochlear Transform

Progress in signal processing seems to converge towards perceptually inspired algorithms. This work proposes a novel signal processing framework, namely Cochlear Transform (CT), inspired by the active spiral cochlear mechanics. CT reflects how the spiral cochlea transforms the input stimuli to spatially supported and tonotopically organized basilar membrane responses, resulting in orthogonal cochlear modes with bio-inspired Time-Frequency Representation (TFR). We treat an input signal as a propagating wave that travels from the cochlear base to apex, along a path that takes a spiral shape from θ = 0◦ to θ = 990◦, respectively. More importantly, we showcase the generalizability of the CT to non-acoustic signals by modulating them to the highest sensitivity region of the cochlea.We try to achieve balance between bio-inspiration and adaptive signal processing. Accordingly, we demonstrate that CT attains important characteristics, such as filterbank with perceptual tuning when excited by White Gaussian Noise, noise robustness, and the cochlear decomposition orthogonality. Numerical examples of synthetic and real signals are given to demonstrate the robustness of CT and comparisons are made with Empirical Wavelet Transform (EWT) and the Variational Mode Decomposition (VMD). CT reveals new unseen and interpretable features not only of acoustic signals, but also real-life biological and seismic signals that stem from the active cochlear mechanics including acoustic distortion effects. This study is the first to demonstrate that mimicking both the active function and the structure of the human cochlear results inenhanced and interpretable TFR resolution, which allows the generalization of the approach to non-acoustic signals. CT resembles spectro-temporal receptive fields necessary for downstream perceptual auditory tasks with a range of application in data analysis, enhancing synergies between machine intelligence and bio-inspired perception. It also paves the way for bio-inspired feature engineering methods, including future CT extensions to applications for multivariate signals and 2D image processing.