Humans and vocal animals use vocalizations to communicate with members of their species. A necessary function of auditory perception is to generalize across the high variability inherent in vocalization production and classify them into behaviorally distinct categories (‘words’ or ‘call types’). Here, we demonstrate that detecting mid-level features in calls achieves production-invariant classification. Starting from randomly chosen marmoset call features, we use a greedy search algorithm to determine the most informative and least redundant features necessary for call classification. High classification performance is achieved using only 10–20 features per call type. Predictions of tuning properties of putative feature-selective neurons accurately match some observed auditory cortical responses. This feature-based approach also succeeds for call categorization in other species, and for other complex classification tasks such as caller identification. Our results suggest that high-level neural representations of sounds are based on task-dependent features optimized for specific computational goals.
Humans and vocal animals use vocalizations (human speech or animal 'calls') to communicate with members of their species. A necessary function of auditory perception is to generalize across the high variability inherent in the production of these sounds and classify them into perceptually distinct categories ('words' or 'call types'). 5 Here, we demonstrate using an information-theoretic approach that production-invariant classification of calls can be achieved by detecting mid-level acoustic features. Starting from randomly chosen marmoset call features, we used a greedy search algorithm to determine the most informative and least redundant set of features necessary for call classification. Call classification at >95% accuracy could be accomplished using only 10 10 -20 features per call type. Most importantly, predictions of the tuning properties of putative neurons selective for such features accurately matched some previously observed responses of superficial layer neurons in primary auditory cortex. Such a feature-based approach succeeded in categorizing calls of other species such as guinea pigs and macaque monkeys, and could also solve other complex classification 15 tasks such as caller identification. Our results suggest that high-level neural representations of sounds are based on task-dependent features optimized for specific computational goals.
The pressure resonance problem impelled by hydraulic pulsation power in piping networks is studied in this thesis. Through theoretical analyzing and computer simulating to the flow variation of multi-pulse sources accumulation, two concepts: the variable initial angle by equal probability and flow pulsation rare, are introduced. Some useful conclusions are also obtained. Most of piping networks vibration in engineering is aroused by the medium pressure pulsation. Destructive violent vibration is set off by the simulation of pressure pulsation when resonant occurred. In order to reduce the vibration, it is important to restrain pressure pulsation and to avoid the resonance areas determined by piping networks construction. On the basis of the optimized approximate model to meet the need of the practical engineering and fluidic network theory, this thesis is mainly concerned with the natural frequency of internal liquid vibration in pipelines. In this thesis a no-damping piping mathematics model as well as the transfer matrix method is employed, and the computer simulation is used in theoretical researching. The simulation software of pressure fluctuation for the complex fluidic transmission systems is developed. The effects of every structure parameters of simulated hydraulic pipelines on the pressure pulsation performance are analyzed in details by using the software which makes us modify some structure parameters efficiently so as to optimize structure, evade resonant, reduce the amplitude of pressure pulsation and avoid fluid resonance. The experiments verify the conclusion of the computer simulation and show that the software is easy to be widely used in the dynamic optimum design of fluid transmission systems.
For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or both adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.
For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated using guinea pig and marmoset vocalizations that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or more adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.
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