Many marine animals use sound passively or actively for communication, foraging, predator avoidance, navigation, and to sense their environment. The advent of acoustic recording tags has allowed biologists to get the on-animal perspective of the sonic environment and, in combination with movement sensors, to relate sounds to the activities of the tagged animal. These powerful tools have led to ...

Economic unit values of soundscape/acoustic effects have been based on changes in the number of annoyed persons or on decibel changes. The normal procedure has been the application of these unit values to noise-attenuation measures affecting the noisier façade of a dwelling. Novel modular vegetation-based soundscape measures, so-called green walls, might be relevant for both noisy and qui...

In this study, we develop a new system for real world audio environment matching. Environment detection within unknown audio streams requires a system that operates in an unsupervised manner since it will be faced with unknown environments without prior information. In addition, the overall solution should be computationally efficient for large audio collection. In the proposed approach, a Gaus...

The performance of current virtual acoustic systems is highly sensitive to the geometry of individual ear at high frequencies. The objective of this paper is to study a virtual acoustic system which may not be sensitive to individual ear shape. The incident sound field around ear is reproduced by using a multi-channel headphone. The results of computer simulations show that the desired sound pr...

Real-world noise may be a mixture of known or trainable noise and unknown unexpected noise. This paper investigates the combination of the conventional noise-reduction techniques with the probabilistic union model to deal with this type of mixed noise for robust speech recognition. In particular, we have developed a multi-environment system to remove the known or trainable acoustic mismatch acr...

Purpose The Reflective Middleware for Acoustic Management (ReM-AM), based on the Cloud Learning Environments (AmICL), aims to improve interaction between users and agents in a Smart Environment (SE) using acoustic services, order consider unpredictable situations due sounds vibrations. middleware allows observing, analyzing, modifying interacting every state of SE from acoustics. This work deta...