Using Matching Pursuit for Estimating Mixing Time Within Room Impulse Responses

Defrance, Guillaume; Daudet, Laurent; Polack, Jean‐Dominique

doi:10.3813/aaa.918239

Cited by 20 publications

(21 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1a and 1b. Estimated TOAs can be used to estimate room geometry [12], the mixing time [14] and other channel properties.…”

Section: Methodsmentioning

confidence: 99%

“…Minimizing the residual error betweenĥ R (n) and h(n) however will give emphasis to describing low-order reflections which have the highest amplitude. In order to account for this,h(n) is created which represents the Energy Decay Curve (EDC) compensated AIR [14]. LASSO is then used to findw that minimizes the expression…”

Section: B Initial Parameter Estimationmentioning

confidence: 99%

“…The solution forw corresponds to the EDC compensated AIR [14]. In order find the vector w which corresponds to the original AIR, the dot product of w and the square-root of the EDC is taken.…”

Section: B Initial Parameter Estimationmentioning

confidence: 99%

“…The proposed method, during this process, does not bound the TOAs to integer sample instances, it models the excitation sound and makes no assumptions about the number of overlapping reflections. These are some of the disadvantages exhibited by existing methods [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sparse parametric modeling of the early part of acoustic impulse responses

Papayiannis

Evers

Naylor

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-Acoustic channels are typically described by their Acoustic Impulse Response (AIR) as a Moving Average (MA) process. Such AIRs are often considered in terms of their early and late parts, describing discrete reflections and the diffuse reverberation tail respectively. We propose an approach for constructing a sparse parametric model for the early part. The model aims at reducing the number of parameters needed to represent it and subsequently reconstruct from the representation the MA coefficients that describe it. It consists of a representation of the reflections arriving at the receiver as delayed copies of an excitation signal. The Time-Of-Arrivals of reflections are not restricted to integer sample instances and a dynamically estimated model for the excitation sound is used. We also present a corresponding parameter estimation method, which is based on regularized-regression and nonlinear optimization. The proposed method also serves as an analysis tool, since estimated parameters can be used for the estimation of room geometry, the mixing time and other channel properties. Experiments involving simulated and measured AIRs are presented, in which the AIR coefficient reconstruction-error energy does not exceed 11.4% of the energy of the original AIR coefficients. The results also indicate dimensionality reduction figures exceeding 90% when compared to a MA process representation.

show abstract

“…1a and 1b. Estimated TOAs can be used to estimate room geometry [12], the mixing time [14] and other channel properties.…”

Section: Methodsmentioning

confidence: 99%

Section: B Initial Parameter Estimationmentioning

confidence: 99%

Section: B Initial Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse parametric modeling of the early part of acoustic impulse responses

Papayiannis

Evers

Naylor

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

show abstract

“…23,29 From the dynamical system point of view, no expression of the transition time exists to the authors' knowledge.…”

Section: Introductionmentioning

confidence: 98%

Numerical evidence of mixing in rooms using the free path temporal distribution

Billon

Embrechts

2011

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

The ergodic propriety of a room has strong effects on its reverberation. If the room is ergodic, the reverberation can be broken up in two steps: a deterministic process followed by a stochastic one. The late reverberation can be then modeled by a reverberation algorithm instead of more computationally consuming methods. In this study, the free path temporal distribution obtained by ray-tracing is used as an indicator of the room's mixing: the energetic average of the path lengths is computed at each time step. Ergodic rooms are thus characterized by rapidly convergent distributions. The free path value becomes independent of time. On the other hand, path selection mechanism and orbits are observed in non-ergodic rooms. The transition time from the deterministic process to the stochastic one is also studied through the evaluation of the room's time constant. It is shown that its value depends only on the mean free path and the boundaries scattering value. An empirical expression is obtained which agrees well with simulations carried out in a concert hall. This transition time from a deterministic model to a stochastic one can be used to speed up the acoustical predictions and auralizations in ergodic rooms.

show abstract

Estimating the Crossover Time Within Room Impulse Responses

Defrance

Polack

2014

Acoustics, Information, and Communication

View full text Add to dashboard Cite

Using Matching Pursuit for Estimating Mixing Time Within Room Impulse Responses

Cited by 20 publications

References 17 publications

Sparse parametric modeling of the early part of acoustic impulse responses

Sparse parametric modeling of the early part of acoustic impulse responses

Numerical evidence of mixing in rooms using the free path temporal distribution

Estimating the Crossover Time Within Room Impulse Responses

Contact Info

Product

Resources

About