Talker's voice and gender stereotype in human auditory sentence processing - evidence from event-related brain potentials

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DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks

Synthetic creation of drum sounds (e.g., in drum machines) is commonly performed using analog or digital synthesis, allowing a musician to sculpt the desired timbre modifying various parameters. Typically, such parameters control low-level features of the sound and often have no musical meaning or perceptual correspondence. With the rise of Deep Learning, data-driven processing of audio emerges as an alternative to traditional signal processing. This new paradigm allows controlling the synthesis process through learned high-level features or by conditioning a model on musically relevant information. In this paper, we apply a Generative Adversarial Network to the task of audio synthesis of drum sounds. By conditioning the model on perceptual features computed with a publicly available feature-extractor, intuitive control is gained over the generation process. The experiments are carried out on a large collection of kick, snare, and cymbal sounds. We show that, compared to a specific prior work based on a U-Net architecture, our approach considerably improves the quality of the generated drum samples, and that the conditional input indeed shapes the perceptual characteristics of the sounds. Also, we provide audio examples and release the code used in our experiments.Comment: 8 pages, 1 figure, 3 tables, accepted in Proc. of the 21st International Society for Music Information Retrieval (ISMIR2020

Analysing the behaviour of robot teams through relational sequential pattern mining

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

Bass Accompaniment Generation via Latent Diffusion

The ability to automatically generate music that appropriately matches an arbitrary input track is a challenging task. We present a novel controllable system for generating single stems to accompany musical mixes of arbitrary length. At the core of our method are audio autoencoders that efficiently compress audio waveform samples into invertible latent representations, and a conditional latent diffusion model that takes as input the latent encoding of a mix and generates the latent encoding of a corresponding stem. To provide control over the timbre of generated samples, we introduce a technique to ground the latent space to a user-provided reference style during diffusion sampling. For further improving audio quality, we adapt classifier-free guidance to avoid distortions at high guidance strengths when generating an unbounded latent space. We train our model on a dataset of pairs of mixes and matching bass stems. Quantitative experiments demonstrate that, given an input mix, the proposed system can generate basslines with user-specified timbres. Our controllable conditional audio generation framework represents a significant step forward in creating generative AI tools to assist musicians in music production

Early parallel processing of auditory word and voice information

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Phase formation and high-temperature stability of very thin co-sputtered Ti-Al and multilayered Ti/Al films on thermally oxidized si substrates

Ti-Al thin films with a thickness of 200 nm were prepared either by co-sputtering from elemental Ti and Al targets or as Ti/Al multilayers with 10 and 20 nm individual layer thickness on thermally oxidized Si substrates. Some of the films were covered with a 20-nm-thick SiO2 layer, which was used as an oxidation protection against the ambient atmosphere. The films were annealed at up to 800 °C in high vacuum for 10 h, and the phase formation as well as the film architecture was analyzed by X-ray diffraction, cross section, and transmission electron microscopy, as well as Auger electron and X-ray photoelectron spectroscopy. The results reveal that the co-sputtered films remained amorphous after annealing at 600 °C independent on the presence of the SiO2 cover layer. In contrast to this, the γ-TiAl phase was formed in the multilayer films at this temperature. After annealing at 800 °C, all films were degraded completely despite the presence of the cover layer. In addition, a strong chemical reaction between the Ti and SiO2 of the cover layer and the substrate took place, resulting in the formation of Ti silicide. In the multilayer samples, this reaction already started at 600 °C

Transparent pointer compression for linked data structures

64-bit address spaces are increasingly important for modern applications, but they come at a price: pointers use twice as much memory, reducing the effective cache capacity and memory bandwidth of the system (compared to 32-bit ad-dress spaces). This paper presents a sophisticated, auto-matic transformation that shrinks pointers from 64-bits to 32-bits. The approach is “macroscopic, ” i.e., it operates on an entire logical data structure in the program at a time. It allows an individual data structure instance or even a subset thereof to grow up to 232 bytes in size, and can compress pointers to some data structures but not others. Together, these properties allow efficient usage of a large (64-bit) ad-dress space. We also describe (but have not implemented) a dynamic version of the technique that can transparently expand the pointers in an individual data structure if it ex-ceeds the 4GB limit. For a collection of pointer-intensive benchmarks, we show that the transformation reduces peak heap sizes substantially by (20 % to 2x) for several of these benchmarks, and improves overall performance significantly in some cases

Enhanced Formation of Nanometric Titanium Cones by Incorporation of Titanium, Tungsten and/or Iron in a Helium Ion Beam

Surface patterning of bio-compatible titanium (Ti) shows a growing interest in the medical field. The engineering of material surfaces can achieve bactericidal properties and osteointegration improvements in order to develop medical implants. Spikes-like surface morphologies have already demonstrated the development of promising bactericidal properties. A barely new method to produce nanometric-sized cones on titanium consists of helium (He) ion irradiation using low energies ( 100 eV) and temperatures comprised between 0.25 T/T 0.5 (with T being the melting temperature of the material). Ti, iron (Fe) and/or tungsten (W) were incorporated in a He beam, and their amounts were quantified using X-ray Photoelectron Spectroscopy (XPS). The He ion energy was varied from 70 and 120 eV, the surface temperatures from 571 to 651 K for fluences approximately equal to 1024 m−2. After irradiation, the surface morphology was characterized using Scanning Electron Microscopy (SEM) and Focused Ion Beam (FIB). This study demonstrated the capability for irradiated Ti surfaces to form cones with tunable density, aspect ratio, and heights with the incorporation of Ti, Fe and/or W in a He ion. Additionally, the growth rate of the cones was enhanced by about 30 times in comparison to pure He irradiation as a function of the chosen materials introduced in the He beam