Accuracy of an acoustic location system for monitoring the position of duetting songbirds in tropical forest

A field test was conducted on the accuracy of an eight-microphone acoustic location system designed to triangulate the position of duetting rufous-and-white wrens (Thryothorus rufalbus) in Costa Rica\u27s humid evergreen forest. Eight microphones were set up in the breeding territories of 20 pairs of wrens, with an average intermicrophone distance of 75.2±2.6 m. The array of microphones was used to record antiphonal duets broadcast through stereo loudspeakers. The positions of the loudspeakers were then estimated by evaluating the delay with which the eight microphones recorded the broadcast sounds. Position estimates were compared to coordinates surveyed with a global-positioning system (GPS). The acoustic location system estimated the position of loudspeakers with an error of 2.82±0.26 m and calculated the distance between the male and female loudspeakers with an error of 2.12±0.42 m. Given the large range of distances between duetting birds, this relatively low level of error demonstrates that the acoustic location system is a useful tool for studying avian duets. Location error was influenced partly by the difficulties inherent in collecting high accuracy GPS coordinates of microphone positions underneath a lush tropical canopy and partly by the complicating influence of irregular topography and thick vegetation on sound transmission. © 2006 Acoustical Society of America

Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on \textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table

Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Extending bioacoustic monitoring of birds aloft through flight call localization with a three-dimensional microphone array

Bioacoustic localization of bird vocalizations provides unattended observations of the location of calling individuals in many field applications. While this technique has been successful in monitoring terrestrial distributions of calling birds, no published study has applied these methods to migrating birds in flight. The value of nocturnal flight call recordings can increase with the addition of three-dimensional position retrievals, which can be achieved with adjustments to existing localization techniques. Using the time difference of arrival method, we have developed a proof-of-concept acoustic microphone array that allows the three-dimensional positioning of calls within the airspace. Our array consists of six microphones, mounted in pairs at the top and bottom of three 10-m poles, arranged in an equilateral triangle with sides of 20 m. The microphone array was designed using readily available components and costs less than $2,000 USD to build and deploy. We validate this technique using a kite-lofted GPS and speaker package, and obtain 60.1% of vertical retrievals within the accuracy of the GPS measurements (+/- 5 m) and 80.4% of vertical retrievals within +/- 10 m. The mean Euclidian distance between the acoustic retrievals of flight calls and the GPS truth was 9.6 m. Identification and localization of nocturnal flight calls have the potential to provide species-specific spatial characterizations of bird migration within the airspace. Even with the inexpensive equipment used in this trial, low-altitude applications such as surveillance around wind farms or oil platforms can benefit from the three-dimensional retrievals provided by this technique

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Incorporation of acoustic sensors in the regulation of a mobile robot

This article introduces the incorporation of acoustic sensors for the localization of a mobile robot. The robot is considered as a sound source and its position is located applying a Time Delay of Arrival (TDOA) method. Since the accuracy of this method varies with the microphone array, a navigation acoustic map that indicates the location errors is built. This map also provides the robot with navigation trajectories point-to-point and the control is capable to drive the robot through these trajectories to a desired configuration. The proposed localization method is thoroughly tested using both a 900 Hz square signal and the natural sound of the robot, which is driven near the desired point with an average error of 0:067 m.This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 01/01/2019, available online: http://www.tandfonline.com/10.1080/01691864.2019.1573703.”Peer ReviewedPostprint (author's final draft