Towards multidimensional attentive voice tracking—estimating voice state from auditory glimpses with regression neural networks and Monte Carlo sampling

Selective attention is a crucial ability of the auditory system. Computationally, following an auditory object can be illustrated as tracking its acoustic properties, e.g., pitch, timbre, or location in space. The difficulty is related to the fact that in a complex auditory scene, the information about the tracked object is not available in a clean form. The more cluttered the sound mixture, the more time and frequency regions where the object of interest is masked by other sound sources. How does the auditory system recognize and follow acoustic objects based on this fragmentary information? Numerous studies highlight the crucial role of top-down processing in this task. Having in mind both auditory modeling and signal processing applications, we investigated how computational methods with and without top-down processing deal with increasing sparsity of the auditory features in the task of estimating instantaneous voice states, defined as a combination of three parameters: fundamental frequency F0 and formant frequencies F1 and F2. We found that the benefit from top-down processing grows with increasing sparseness of the auditory data.