Psychoacoustic Model Research Articles

Automatic speech recognition using psychoacoustic models.

An approach to automatic speech recognition is described, which, in a straightforward way, follows the concept of (1) preprocessing in terms of auditory parameters and (2) subsequent classification and recognition. The preprocessing system has been realized in analog hardware, while recognition is carried out on a digital computer. In the preprocessing system, the essential psychoacoustic principles of the perception of loudness, pitch, roughness, and subjective duration are implemented with some approximation. The system essentially consists of 24 bandpass filters, nonlinear transformation of each filter output into specific loudness and specific roughness, and final transformation of these parameters into total loudness, total roughness, and three spectral momenta. As a means to further reduce the information flow, continuous selection of dominant parameters is also considered on the basis of psychoacoustic data. The subsequent recognition process is mainly characterized by (1) discrimination between speech and silent periods, (2) detection of syllable peaks and classification of syllable nuclei, and (3) assumption of syllable boundaries and classification of consonant clusters. Though the entire system as yet is far from being complete and perfect, the present results indicate that the concept provides a systematic and promising way towards automatic recognition of continuous speech.

Loudspeaker measurements weighted by psychoacoustic modeling

Modern data acquisition methods permit comprehensive measurements of prototype loudspeakers in various acoustical environments. Digitization and subsequent transformations appear to offer new information about loudspeaker spectral output as a function of time. However, problems of interpretation exist, as with classical measurement methods, especially when the details of complex displays are considered. By convolving acquired data with models of psychoacoustic processes, subjectively important features of the data are disclosed with improved contrast.

Sonar for generalized target description and its similarity to animal echolocation systems

Range‐distributed targets are often characterized arrays of point scatterers. This conceptualization corresponds to a target impulse response that is a sequence of impulses. A physically meaningful generalization of this description is obtained by modeling an impulse response in terms of pulse doublets, impulses, step functions, ramp functions, etc., i.e., as a superposition of delayed impulses and differentiated or integrated impulses. The resulting impulse response model resembles a spline function without continuity conditions. A sonar system for generalized target characterization is described; signals and receivers are specified for (1) optimum estimation of reflector parameters and (2) detection of a known target in clutter. The derived signals correspond closely with some of the waveforms that are used by dolphins and bats for echolocation. The corresponding echo analyzers are similar to some psychoacoustic models of the mammalian hearing system.