This study investigates the human ability to perceive biological movements through friction sounds produced by drawings and, furthermore, the ability to recover drawn shapes from the friction sounds generated. In a first experiment, friction sounds, real-time synthesized and modulated by the velocity profile of the drawing gesture, revealed that subjects associated a biological movement to those sounds whose timbre variations were generated by velocity profiles following the 1/3 power law. This finding demonstrates that sounds can adequately inform about human movements if their acoustic characteristics are in accordance with the kinematic rule governing actual movements. Further investigations of our ability to recognize drawn shapes were carried out in 2 association tasks in which both recorded and synthesized sounds had to be associated to both distinct and similar visual shapes. Results revealed that, for both synthesized and recorded sounds, subjects made correct associations for distinct shapes, although some confusion was observed for similar shapes. The comparisons made between recorded and synthesized sounds lead to conclude that the timbre variations induced by the velocity profile enabled the shape recognition. The results are discussed in the context of the ecological and ideomotor frameworks.

In this article, we propose a control strategy for synthesized continuous-interaction sounds. The framework of our research is based on the action–object paradigm that describes the sound as the result of an action on an object and that presumes the existence of sound invariants (i.e., perceptually relevant signal morphologies that carry information about the action's or the object's attributes). Auditory cues are investigated here for the evocations of rubbing, scratching, and rolling interactions. A generic sound-synthesis model that simulates these interactions is detailed. We then suggest an intuitive control strategy that enables users to navigate continuously from one interaction to another in an “action space,” thereby offering the possibility to simulate morphed interactions—for instance, ones that morph between rubbing and rolling.

Natural soundscapes correspond to the acoustical patterns produced by biological and geophysical sound sources at different spatial and temporal scales for a given habitat. This pilot study aims to characterize the temporal-modulation information available to humans when perceiving variations in soundscapes within and across natural habitats. This is addressed by processing soundscapes from a previous study [Krause, Gage, and Joo. (2011). Landscape Ecol. 26, 1247] via models of human auditory processing extracting modulation at the output of cochlear filters. The soundscapes represent combinations of elevation, animal, and vegetation diversity in four habitats of the biosphere reserve in the Sequoia National Park (Sierra Nevada, USA). Bayesian statistical analysis and support vector machine classifiers indicate that: (i) amplitude-modulation (AM) and frequency-modulation (FM) spectra distinguish the soundscapes associated with each habitat; and (ii) for each habitat, diurnal and seasonal variations are associated with salient changes in AM and FM cues at rates between about 1 and 100 Hz in the low (1–3 kHz) audio-frequency range. Support vector machine classifications further indicate that soundscape variations can be classified accurately based on these perceptually inspired representations.

The ability of a listener to recognize sound sources, and in particular musical instruments from the sounds they produce, raises the question of determining the acoustical information used to achieve such a task. It is now well known that the shapes of the temporal and spectral envelopes are crucial to the recognition of a musical instrument. More recently, Modulation Power Spectra (MPS) have been shown to be a representation that potentially explains the perception of musical instrument sounds. Nevertheless, the question of which specific regions of this representation characterize a musical instrument is still open. An identification task was applied to two subsets of musical instruments: tuba, trombone, cello, saxophone, and clarinet on the one hand, and marimba, vibraphone, guitar, harp, and viola pizzicato on the other. The sounds were processed with filtered spectrotemporal modulations with 2D Gaussian windows. The most relevant regions of this representation for instrument identification were determined for each instrument and reveal the regions essential for their identification. The method used here is based on a “molecular approach”, the so-called bubbles method. Globally, the instruments were correctly identified and the lower values of spectrotemporal modulations are the most important regions of the MPS for recognizing instruments. Interestingly, instruments that were confused with each other led to non-overlapping regions and were confused when they were filtered in the most salient region of the other instrument. These results suggest that musical instrument timbres are characterized by specific spectrotemporal modulations, information which could contribute to music information retrieval tasks such as automatic source recognition.