A Presence- and Performance-Driven Framework to Investigate Interactive Networked Music Learning Scenarios

Cooperative music making in networked environments has been subject of extensive research, scientific and artistic. Networked music performance (NMP) is attracting renewed interest thanks to the growing availability of effective technology and tools for computer-based communications, especially in the area of distance and blended learning applications. We propose a conceptual framework for NMP research and design in the context of classical chamber music practice and learning: presence-related constructs and objective quality metrics are used to problematize and systematize the many factors affecting the experience of studying and practicing music in a networked environment. To this end, a preliminary NMP experiment on the effect of latency on chamber music duos experience and quality of the performance is introduced. The degree of involvement, perceived coherence, and immersion of the NMP environment are here combined with measures on the networked performance, including tempo trends and misalignments from the shared score. Early results on the impact of temporal factors on NMP musical interaction are outlined, and their methodological implications for the design of pedagogical applications are discussed

Whole genome sequencing to investigate the emergence of clonal complex 23 Neisseria meningitidis serogroup Y disease in the United States

In the United States, serogroup Y, ST-23 clonal complex Neisseria meningitidis was responsible for an increase in meningococcal disease incidence during the 1990s. This increase was accompanied by antigenic shift of three outer membrane proteins, with a decrease in the population that predominated in the early 1990s as a different population emerged later in that decade. To understand factors that may have been responsible for the emergence of serogroup Y disease, we used whole genome pyrosequencing to investigate genetic differences between isolates from early and late N. meningitidis populations, obtained from meningococcal disease cases in Maryland in the 1990s. The genomes of isolates from the early and late populations were highly similar, with 1231 of 1776 shared genes exhibiting 100% amino acid identity and an average πN = 0.0033 and average πS = 0.0216. However, differences were found in predicted proteins that affect pilin structure and antigen profile and in predicted proteins involved in iron acquisition and uptake. The observed changes are consistent with acquisition of new alleles through horizontal gene transfer. Changes in antigen profile due to the genetic differences found in this study likely allowed the late population to emerge due to escape from population immunity. These findings may predict which antigenic factors are important in the cyclic epidemiology of meningococcal disease

A Novel Phase Variation Mechanism in the Meningococcus Driven by a Ligand-Responsive Repressor and Differential Spacing of Distal Promoter Elements

Phase variable expression, mediated by high frequency reversible changes in the length of simple sequence repeats, facilitates adaptation of bacterial populations to changing environments and is frequently important in bacterial virulence. Here we elucidate a novel phase variable mechanism for NadA, an adhesin and invasin of Neisseria meningitidis. The NadR repressor protein binds to operators flanking the phase variable tract and contributes to the differential expression levels of phase variant promoters with different numbers of repeats likely due to different spacing between operators. We show that IHF binds between these operators, and may permit looping of the promoter, allowing interaction of NadR at operators located distally or overlapping the promoter. The 4-hydroxyphenylacetic acid, a metabolite of aromatic amino acid catabolism that is secreted in saliva, induces NadA expression by inhibiting the DNA binding activity of the repressor. When induced, only minor differences are evident between NadR-independent transcription levels of promoter phase variants and are likely due to differential RNA polymerase contacts leading to altered promoter activity. Our results suggest that NadA expression is under both stochastic and tight environmental-sensing regulatory control, both mediated by the NadR repressor, and may be induced during colonization of the oropharynx where it plays a major role in the successful adhesion and invasion of the mucosa. Hence, simple sequence repeats in promoter regions may be a strategy used by host-adapted bacterial pathogens to randomly switch between expression states that may nonetheless still be induced by appropriate niche-specific signals

Intelligent Networked Music Performance Experiences

A Networked Music Performance (NMP) is defined as what happens when geographically displaced musicians interact together while connected via network. The first NMP experiments begun in the 1970s, however, only recently the development of network communication technologies has created the necessary infrastructure needed to successfully create an NMP. Moreover, the widespread adoption of network-based interactions during the COVID-19 pandemic has generated a renewed interest towards distant music-based interaction. In this chapter we present the Intelligent networked Music PERforMANce experiENCEs (IMPERMANENCE) as a comprehensive NMP framework that aims at creating a compelling performance experience for the musicians. In order to do this, we first develop the neTworkEd Music PErfoRmANCe rEsearch (TEMPERANCE) framework in order to understand which are the main needs of the participants in a NMP. Informed by these results we then develop IMPERMANENCE accordingly

A Deep Learning-Based Pressure Matching Approach To Soundfield Synthesis

In this paper we propose a technique for soundfield synthesis based on the combination of the Pressure Matching (PM) approach and of deep learning-based methods. The pressure matching approach retrieves the driving signals for soundfield reproduction by minimizing the reproduction error at discrete control points through least squares. In this paper we follow a similar approach, but we perform the minimization by applying a Convolutional Neural Network (CNN). Through simulations, we compare the performance of the original pressure matching approach with the proposed technique and demonstrate how the latter is able to overcome spatial aliasing issues

His engine’s voice: towards a vocal sketching tool for synthetic engine sounds

We present a work in progress on a sound design tool for automotive applications, which allows the designer to rapidly prototype synthetic engine sounds through the use of vocal imitations. A physics-based model for engine sound synthesis, with real time parametric control based on timbral descriptors extracted from the vocal signal, is under development. This work can be considered as a preliminary study in the larger context of the SkAT-VG EU project, whose goal is to find ways to exploit and develop the use of voice as a sketching and prototyping tool in sound design practices

Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks

The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized cross-correlations (GCCs) have been widely used for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a novel technique for TDE based on a sub-band analysis of the cross-power spectrum phase, providing a structured two-dimensional representation of the time delay information contained across different frequency bands. Inspired by deep-learning-based image denoising solutions, we propose in this paper the use of convolutional neural networks (CNNs) to learn the time-delay patterns contained in FS-GCCs extracted in adverse acoustic conditions. Our experiments confirm that the proposed approach provides excellent TDE performance while being able to generalize to different room and sensor setups

Frequency-Sliding Generalized Cross-Correlation: A Sub-Band Time Delay Estimation Approach

The generalized cross-correlation(GCC) is regarded as the most popular approach for estimating the time difference of arrival (TDOA) between the signals received at two sensors. Time delay estimates are obtained by maximizing the GCC output, where the direct-path delay is usually observed as a prominent peak. Moreover, GCCs play also an important role in steered response power (SRP) localization algorithms, where the SRP functional can be written as an accumulation of the GCCs computed from multiple sensor pairs. Unfortunately, the accuracy of TDOA estimates is affected by multiple factors, including noise, reverberation and signal bandwidth. In this paper, a sub-band approach for time delay estimation aimed at improving the performance of the conventional GCC is presented. The proposed method is based on the extraction of multiple GCCs corresponding to different frequency bands of the cross-power spectrum phase in a sliding-window fashion. The major contributions of this paper include: 1) a sub-band GCC representation of the cross-power spectrum phase that, despite having a reduced temporal resolution, provides a more suitable representation for estimating the true TDOA; 2) such matrix representation is shown to be rank one in the ideal noiseless case, a property that is exploited in more adverse scenarios to obtain a more robust and accurate GCC; 3) we propose a set of low-rank approximation alternatives for processing the sub-band GCC matrix, leading to better TDOA estimates and source localization performance. An extensive set of experiments is presented to demonstrate the validity of the proposed approach

Reconstructing speech from CNN embeddings

The complete understanding of the decision-making process of Convolutional Neural Networks (CNNs) is far from being fully reached. Many researchers proposed techniques to interpret what a network actually 'learns' from data. Nevertheless many questions still remain unanswered. In this work we study one aspect of this problem by reconstructing speech from the intermediate embeddings computed by a CNNs. Specifically, we consider a pre-trained network that acts as a feature extractor from speech audio. We investigate the possibility of inverting these features, reconstructing the input signals in a black-box scenario, and quantitatively measure the reconstruction quality by measuring the word-error-rate of an off-the-shelf ASR model. Experiments performed using two different CNN architectures trained for six different classification tasks, show that it is possible to reconstruct time-domain speech signals that preserve the semantic content, whenever the embeddings are extracted before the fully connected layers

[In vitro and in vivo antibacterial activity of cephapirin-dicloxacillin combination in the ratio 1:1].

Studies were carried out on the combination, 1:1 ratio, of two antibiotics: cephapirin and dicloxacillin. After the analysis of the activity spectrum "in vitro" of each antibiotics, the synergism of this combination against the Gram-negative bacteria strains was clearly demonstrated. This combination demonstrated a particular activity against the beta-lactamases producing strains "in vitro" as well in protection tests "in vivo" in experimental infections in mice