Evolution and Ecology of Silent Flight in Owls and Other Flying Vertebrates

Synopsis We raise and explore possible answers to three questions about the evolution and ecology of silent flight of owls: (1) do owls fly silently for stealth, or is it to reduce self-masking? Current evidence slightly favors the self-masking hypothesis, but this question remains unsettled. (2) Two of the derived wing features that apparently evolved to suppress flight sound are the vane fringes and dorsal velvet of owl wing feathers. Do these two features suppress aerodynamic noise (sounds generated by airflow), or do they instead reduce structural noise, such as frictional sounds of feathers rubbing during flight? The aerodynamic noise hypothesis lacks empirical support. Several lines of evidence instead support the hypothesis that the velvet and fringe reduce frictional sound, including: the anatomical location of the fringe and velvet, which is best developed in wing and tail regions prone to rubbing, rather than in areas exposed to airflow; the acoustic signature of rubbing, which is broadband and includes ultrasound, is present in the flight of other birds but not owls; and the apparent relationship between the velvet and friction barbules found on the remiges of other birds. (3) Have other animals also evolved silent flight? Wing features in nightbirds (nocturnal members of Caprimulgiformes) suggest that they may have independently evolved to fly in relative silence, as have more than one diurnal hawk (Accipitriformes). We hypothesize that bird flight is noisy because wing feathers are intrinsically predisposed to rub and make frictional noise. This hypothesis suggests a new perspective: rather than regarding owls as silent, perhaps it is bird flight that is loud. This implies that bats may be an overlooked model for silent flight. Owl flight may not be the best (and certainly, not the only) model for “bio-inspiration” of silent flight.

Discrimination of fabric frictional sounds based on Haar features

In allusion to the challenging issue of identifying fabric materials by frictional sounds, this study endeavors to prove the possibility of classifying fabric friction sounds into their material categories using discriminators built upon the Haar features. A total of 32 pieces of fabric falling into four material categories including cotton, wool, silk, and flax are put through a specialized apparatus to collect frictional sound signals. The Haar features on every scale and position of the acquired signal are extracted to establish a feature space. For each point in the feature space, a discriminator is built to approve all positive samples of a certain category and deny as many negative samples as possible. To relieve the heavy burden produced by the huge number of discriminators, progressive selection is performed on the discriminators to form a queue in which a discriminator is liable to fix some errors of the former. The outcome is a much-reduced version of the unordered discriminators with the same discriminability. The improved Haar feature is also investigated and is found to be capable of reducing the size of the discrimination queue, thus further improving the efficiency of the mechanism. It is also revealed that additional samples involved can help achieve a perfect accuracy. The discrimination mechanism advanced by this effort can provide a basis for identifying fabric materials by frictional sounds.

Comparative Study on the Frictional Sound Properties of Woven Fabrics

An innovative Frictional Sound Automatic Measuring System (FSAMS) was designed to collect and enable analysis of the frictional sound spectra of four natural fibre woven fabrics which included cotton, linen, silk, and wool. The Fast Fourier Transform (FFT) method was used to convert time-domain signals into frequency-domain signals to enable the maximum sound amplitude (MSA) and the level pressure of the total sound (LPTS) of the cotton, linen, silk, and wool fabrics to be calculated and analysed. Subsequently auto-regression formulae were used to calculate the fabric auto-regressive coefficients (ARC, ARF, and ARE); the correlations between fabric frictional sound in terms of LPTS and AR coefficients, and mechanical properties as measured by KES-FB were also evaluated. Stepwise regression was then used to identify the key frictional sound parameters for the four types of fabric. The results show that LPTS values for cotton, linen, silk, and wool fabrics increase with their ARC values. It was revealed that the key mechanical parameters affecting fabric frictional sound for the four natural fibre woven fabrics were not the same for each fabric type: the parameters that influenced LPTS values were the fabric weight and bending hysteresis for the cotton fabric, tensile energy for the linen, tensile resilience for the silk and shear hysteresis at a 5° shear angle for the wool fabric.

Study on the Friction Sound Properties of Natural-Fiber Woven Fabrics

An innovative frictional sound automatic measuring system (FSAMS) was designed and used in this study to investigate the frictional sound generated when natural-fibre woven fabrics are rubbed together. Frictional sound measurements made using the automatic FSAMS were compared with those from a manual frictional sound measuring system (Manual FSAMS). The frictional sounds of four natural-fiber woven fabrics (i.e., cotton, linen, silk, and wool) were recorded; the Fast Fourier Transform method was used to convert time domain signals into frequency domain signals, and the maximum sound amplitude (MSA) and level pressure of the total sound (LPTS) of cotton, linen, silk, and wool were calculated. The results of a t test, analysis of variance, data reproducibility, and cluster spectrums measured from the four natural-fiber woven fabrics were compared for the two test equipment systems. The results from the t test and analysis of variance showed significant differences in the MSA and LPTS measured. Data reproducibility was superior to the automatic FSAMS compared with the manual FSAMS, and the cluster spectrums were more readily distinguishable.

Frictional Sound Analysis by Simulating the Human Arm Movement

Fabric noise generated by fabric-to-fabric friction is considered as one of the auditory disturbances that can have an impact on the quality of some textile products. For this reason, an instrument has been developed to analyse this phenomenon. The instrument is designed to simulate the relative movement of a human arm when walking. In order to understand the nature of the relative motion of a human arm, films of the upper half of the human body were taken. These films help to define the parameters required for movement simulation. These parameters are movement trajectory, movement velocity, arm pressure applied on the lateral part of the trunk and the friction area. After creating the instrument, a set of soundtracks related to the noise generated by fabric-to-fabric friction was recorded. The recordings were treated with a specific software to extract the sound parameters and the acoustic imprints of fabric were obtained.