K. Drossos, A. Floros, and N. Kanellopoulos, “A Loudness-based Adaptive Equalization Technique for Subjectively Improved Sound Reproduction," in proceedings of the Audio Engineering Society (AES) 136th convention, Apr. 26–29, Berlin, Germany, 2014.
Sound equalization is a common approach for objectively or subjectively defining the reproduction level at specific frequency bands. It is also well-known that the human auditory system demonstrates an inner process of sound-weighting. Due to this, the perceived loudness changes with the frequency and the user-defined sound reproduction gain, resulting into a deviation of the intended and the perceived equalization scheme as the sound level changes. In this work we introduce a novel equalization approach that takes into account the above perceptual loudness effect in order to achieve subjectively constant equalization. A series of listening tests shows that the proposed equalization technique is an efficient and listener-preferred alternative for both professional and home audio reproduction applications.
K. Drossos, A. Floros, S. Potirakis, N. Tatlas, and G. Tuna, “A socially-intelligent multirobot service team for in-home monitoring," in proceedings of the 5th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA), Jul. 9–7, Chania, Greece, 2014.
The objective of this study is to develop a socially-intelligent service team comprised of multiple robots with sophisticated sonic interaction capabilities that aims to transparently collaborate towards efficient and robust monitoring by close interaction. In the distributed scenario proposed in this study, the robots share any acoustic data extracted from the environment and act in-sync with the events occurring in their living environment in order to provide potential means for efficient monitoring and decision-making within a typical home enclosure. Although each robot acts as an individual recognizer using a novel emotionally-enriched word recognition system, the final decision is social in nature and is followed by all. Moreover, the social decision stage triggers actions that are algorithmically distributed among the robots' population and enhances the overall approach with the potential advantages of the team work within specific communities through collaboration.
K. Drossos, A. Floros, and A. Giannakoulopoulos, “BEADS: A Dataset of Binaural Emotionally Annotated Digital Sounds," in proceedings of the 5th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA), Jul. 9–7, Chania, Greece, 2014.
Emotion recognition from generalized sounds is an interdisciplinary and emerging field of research. A vital requirement for this kind of investigations is the availability of ground truth datasets. Currently, there are 2 freely available datasets of emotionally annotated sounds, which, however, do not include sound evenets (SEs) with manifestation of the spatial location of the source. The latter is an inherent natural component of SEs, since all sound sources in real-world conditions are physically located and perceived somewhere in the listener's surrounding space. In this work we present a novel emotionally annotated sounds dataset consisting of 32 SEs that are spatially rendered using appropriate binaural processing. All SEs in the dataset are available in 5 spatial positions corresponding to source/receiver angles equal to 0, 45, 90, 135 and 180 degrees. We have used the IADS dataset as the initial collection of SEs prior to binaural processing. The annotation measures obtained for the novel binaural dataset demonstrate a significant accordance with the existing IADS dataset, while small ratings declinations illustrate a perceptual adaptation imposed by the more realistic SEs spatial representation.
M. Kaliakatsos–Papakostas, A. Floros, K. Drossos, K. Koukoudis, M. Kuzalas, and A. Kalantzis, “Swarm Lake: A Game of Swarm Intelligence, Human Interaction and Collaborative Music Composition," in proceedings of the Joint Conference ICMC/SMC 2014, Sep. 14–20, Athens, Greece, 2014.
In this work we aim to combine a game platform with the concept of collaborative music synthesis. We use bio-inspired intelligence for developing a world - the Lake - where multiple tribes of artiﬁcial, autonomous agents live within, having survival as their ultimate goal. The tribes exhibit primitive social swarm-based behavior and intelligence, which is used for taking actions that will potentially allow to dominate the game world. Tribes’ populations also demonstrate a number of physical properties that re-strict their ability to act illimitably. Multiuser interventionis employed in parallel, affecting the automated decisions and the physical parameters of the tribes, thus infusing the gaming orientation of the application context. Finally,sound synthesis is achieved through a complex mapping scheme established between the events occurring in the Lake and the rhythmic, harmonic and dynamic-range parameters of an advanced, collaborative sound composition engine. This complex mapping scheme allows the production of interesting and complicated sonic patterns that fol-low the performance evolution in both objective and conceptual levels. The overall synthesis process is controlled by the conductor, a virtual entity that determines the synthesis evolution in a way that is very similar to directing an ensemble performance in real world.