Modelling modulation perception : modulation low-pass filter or modulation filter bank?

In current models of modulation perception, the stimuli are first filtered and nonlinearly transformed (mostly half-wave rectified). In order to model the low-pass characteristic of measured modulation transfer functions, the next stage in the models is a first-order low-pass filter with a typical cutoff frequency of 50 to 60 Hz. From physiological studies in mammals it is known that many neurons in, e.g., the inferior colliculus, show a bandpass characteristic in their sensitivity to amplitude modulation. Results from psychophysical studies of modulation masking also suggest some kind of bandpass analysis of modulation frequencies. Results of two experiments on modulation detection that allow discrimination between models incorporating a low-pass filter and those using a modulation filterbank are presented. In the first experiment, modulation detection thresholds were measured for noise carriers of bandwidths between 3 and 6000 Hz. In the second experiment, modulation detection for a sinusoidal carrier was measured in the presence of interfering modulation components with a bandpass characteristic in the modulation spectrum. The results from these experiments could not be simulated by a model including a modulation low-pass filter, but were successfully simulated by a model using a modulation filterbank

Joint estimation of reverberation time and early-to-late reverberation ratio from single-channel speech signals

The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters commonly used to characterize acoustic room environments. In contrast to conventional blind estimation methods that process the two parameters separately, we propose a model for joint estimation to predict the RT and the ELR simultaneously from single-channel speech signals from either full-band or sub-band frequency data, which is referred to as joint room parameter estimator (jROPE). An artificial neural network is employed to learn the mapping from acoustic observations to the RT and the ELR classes. Auditory-inspired acoustic features obtained by temporal modulation filtering of the speech time-frequency representations are used as input for the neural network. Based on an in-depth analysis of the dependency between the RT and the ELR, a two-dimensional (RT, ELR) distribution with constrained boundaries is derived, which is then exploited to evaluate four different configurations for jROPE. Experimental results show that-in comparison to the single-task ROPE system which individually estimates the RT or the ELR-jROPE provides improved results for both tasks in various reverberant and (diffuse) noisy environments. Among the four proposed joint types, the one incorporating multi-task learning with shared input and hidden layers yields the best estimation accuracies on average. When encountering extreme reverberant conditions with RTs and ELRs lying beyond the derived (RT, ELR) distribution, the type considering RT and ELR as a joint parameter performs robustly, in particular. From state-of-the-art algorithms that were tested in the acoustic characterization of environments challenge, jROPE achieves comparable results among the best for all individual tasks (RT and ELR estimation from full-band and sub-band signals)

Exploring auditory-inspired acoustic features for room acoustic parameter estimation from monaural speech

Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters. In this paper, we propose a blind ROom Parameter Estimator (ROPE) based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses (measured versus simulated), mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments (ACE) challenge, the ROPE model is the only one that is among the best for all individual tasks (RT and ELR estimation from fullband and subband signals). Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications

The spatial relation between the event horizon and trapping horizon

The relation between event horizons and trapping horizons is investigated in a number of different situations with emphasis on their role in thermodynamics. A notion of constant change is introduced that in certain situations allows the location of the event horizon to be found locally. When the black hole is accreting matter the difference in area between the two different horizons can be many orders of magnitude larger than the Planck area. When the black hole is evaporating the difference is small on the Planck scale. A model is introduced that shows how trapping horizons can be expected to appear outside the event horizon before the black hole starts to evaporate. Finally a modified definition is introduced to invariantly define the location of the trapping horizon under a conformal transformation. In this case the trapping horizon is not always a marginally outer trapped surface.Comment: 16 pages, 1 figur

Effect of competitive acoustic environments on speech intelligibility

Excessive noise and reverberation times degrade listening abilities in everyday life environments. This is particularly true for school settings. Most classrooms in Italy are settled in historical buildings that generate competitive acoustic environments. So far, few studies investigated the effect of real acoustics on speech intelligibility and on the spatial release from masking, focusing more on laboratory conditions. Also, the effect of noise on speech intelligibility was widely investigated considering its energetic rather than its informational content. Therefore, a study involving normal hearing adults was performed presenting listening tests via headphone and considering the competitive real acoustics of two primary-school classrooms with reverberation time of 0.4 s and 3.1 s, respectively. The main objective was the investigation of the effect of reverberation and noise on the spatial release from masking to help the design of learning environments. Binaural room impulse responses were acquired, with noise sources at different azimuths from the listener’s head. The spatial release from masking was significantly affected by noise type and reverberation. Longer reverberation times brought to worst speech intelligibility, with speech recognition thresholds higher by 6 dB on average. Noise with an informational content was detrimental by 7 dB with respect to an energetic noise

Evidence for a Hard Ionizing Spectrum from a z=6.11 Stellar Population

We present the Magellan/FIRE detection of highly-ionized CIV 1550 and OIII] 1666 in a deep infrared spectrum of the z=6.11 gravitationally lensed low-mass galaxy RXC J2248.7-4431-ID3, which has previously-known Lyman-alpha. No corresponding emission is detected at the expected location of HeII 1640. The upper limit on HeII paired with detection of OIII] and CIV constrains possible ionization scenarios. Production of CIV and OIII] requires ionizing photons of 2.5-3.5 Ryd, but once in that state their multiplet emission is powered by collisional excitation at lower energies (~0.5 Ryd). As a pure recombination line, HeII emission is powered by 4 Ryd ionizing photons. The data therefore require a spectrum with significant power at 3.5 Ryd but a rapid drop toward 4.0 Ryd. This hard spectrum with a steep drop is characteristic of low-metallicity stellar populations, and less consistent with soft AGN excitation, which features more 4 Ryd photons and hence higher HeII flux. The conclusions based on ratios of metal line detections to Helium non-detection are strengthened if the gas metallicity is low. RXJ2248-ID3 adds to the growing handful of reionization-era galaxies with UV emission line ratios distinct from the general z=2-3 population, in a way that suggests hard ionizing spectra that do not necessarily originate in AGN.Comment: 7 pages, 4 figures, 1 table. Accepted for publication to ApJ