577 research outputs found
Insights on the Neuromagnetic Representation of Temporal Asymmetry in Human Auditory Cortex.
Communication sounds are typically asymmetric in time and human listeners are highly sensitive to this short-term temporal asymmetry. Nevertheless, causal neurophysiological correlates of auditory perceptual asymmetry remain largely elusive to our current analyses
and models. Auditory modelling and animal electrophysiological recordings suggest that perceptual asymmetry results from the presence of multiple time scales of temporal integration, central to the auditory periphery. To test this hypothesis we recorded auditory evoked fields (AEF) elicited by asymmetric sounds in humans. We found a strong correlation between perceived tonal salience of ramped and damped sinusoids and the AEFs, as quantified by the amplitude of the N100m dynamics. The N100m amplitude increased with stimulus
half-life time, showing a maximum difference between the ramped and damped stimulus for a modulation half-life time of 4 ms which is greatly reduced at 0.5 ms and 32 ms. This behaviour of the N100m closely parallels psychophysical data in a manner that: i) longer
half-life times are associated with a stronger tonal percept, and ii) perceptual differences between damped and ramped are maximal at 4 ms half-life time. Interestingly, differences in evoked fields were significantly stronger in the right hemisphere, indicating some degree of hemispheric specialisation. Furthermore, the N100m magnitude was successfully
explained by a pitch perception model using multiple scales of temporal integration of auditory
nerve activity patterns. This striking correlation between AEFs, perception, and model predictions suggests that the physiological mechanisms involved in the processing of pitch evoked by temporal asymmetric sounds are reflected in the N100m
High-resolution sinusoidal analysis for resolving harmonic collisions in music audio signal processing
Many music signals can largely be considered an additive combination of
multiple sources, such as musical instruments or voice. If the musical sources
are pitched instruments, the spectra they produce are predominantly harmonic,
and are thus well suited to an additive sinusoidal model. However,
due to resolution limits inherent in time-frequency analyses, when the harmonics
of multiple sources occupy equivalent time-frequency regions, their
individual properties are additively combined in the time-frequency representation
of the mixed signal. Any such time-frequency point in a mixture
where multiple harmonics overlap produces a single observation from which
the contributions owed to each of the individual harmonics cannot be trivially
deduced. These overlaps are referred to as overlapping partials or harmonic
collisions. If one wishes to infer some information about individual sources in
music mixtures, the information carried in regions where collided harmonics
exist becomes unreliable due to interference from other sources. This interference
has ramifications in a variety of music signal processing applications
such as multiple fundamental frequency estimation, source separation, and
instrumentation identification.
This thesis addresses harmonic collisions in music signal processing applications.
As a solution to the harmonic collision problem, a class of signal
subspace-based high-resolution sinusoidal parameter estimators is explored.
Specifically, the direct matrix pencil method, or equivalently, the Estimation
of Signal Parameters via Rotational Invariance Techniques (ESPRIT)
method, is used with the goal of producing estimates of the salient parameters
of individual harmonics that occupy equivalent time-frequency regions. This
estimation method is adapted here to be applicable to time-varying signals
such as musical audio. While high-resolution methods have been previously
explored in the context of music signal processing, previous work has not
addressed whether or not such methods truly produce high-resolution sinusoidal parameter estimates in real-world music audio signals. Therefore, this
thesis answers the question of whether high-resolution sinusoidal parameter
estimators are really high-resolution for real music signals.
This work directly explores the capabilities of this form of sinusoidal parameter
estimation to resolve collided harmonics. The capabilities of this
analysis method are also explored in the context of music signal processing
applications. Potential benefits of high-resolution sinusoidal analysis are
examined in experiments involving multiple fundamental frequency estimation
and audio source separation. This work shows that there are indeed
benefits to high-resolution sinusoidal analysis in music signal processing applications,
especially when compared to methods that produce sinusoidal
parameter estimates based on more traditional time-frequency representations.
The benefits of this form of sinusoidal analysis are made most evident
in multiple fundamental frequency estimation applications, where substantial
performance gains are seen. High-resolution analysis in the context of
computational auditory scene analysis-based source separation shows similar
performance to existing comparable methods
Modal Decompositions of Impulse Responses for Parametric Interaction
A modelling system for the impulse responses (IRs) of reverberators is presented. The overarching purpose of this system is to offer similar levels of control over captured IRs to that of algorithmic reverberators whilst retaining their acoustic plausibility and, where desired, realism. Specifically, an approach to estimating the parameters of the model is presented which offers a significant reduction in the computational requirements of the matrix decomposition method ESPRIT, whilst offering vastly improved quality than is possible by using a single Fourier analysis. These methods are compared, first on large sets of short-duration synthetic signals, and then on a wide range of typical IRs, some many seconds in duration. Finally, systems that employ the model described and the analysis method it uses, are discussed
Multichannel high resolution NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain
Several probabilistic models involving latent components have been proposed for modeling time-frequency (TF) representations of audio signals such as spectrograms, notably in the nonnegative matrix factorization (NMF) literature. Among them, the recent high-resolution NMF (HR-NMF) model is able to take both phases and local correlations in each frequency band into account, and its potential has been illustrated in applications such as source separation and audio inpainting. In this paper, HR-NMF is extended to multichannel signals and to convolutive mixtures. The new model can represent a variety of stationary and non-stationary signals, including autoregressive moving average (ARMA) processes and mixtures of damped sinusoids. A fast variational expectation-maximization (EM) algorithm is proposed to estimate the enhanced model. This algorithm is applied to piano signals, and proves capable of accurately modeling reverberation, restoring missing observations, and separating pure tones with close frequencies
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