Estimation of Room Acoustic Parameters: The ACE Challenge

Eaton, James; Gaubitch, Nikolay D.; Moore, Alastair H.; Naylor, Patrick A.

doi:10.1109/taslp.2016.2577502

Cited by 135 publications

(119 citation statements)

References 27 publications

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“…We compare our proposed CNN network trained on our data (Our CNN + AIRA) with previously published state-of-theart results from the ACE challenge [5], previously published GT-CNN results [10] for T60 only, and our reimplementation of the GT-CNN [10] estimator trained on our augmented dataset (GT-CNN + AIRA) for both T60 and DRR. Table 2 and Table 3 show T60 and DRR estimation bias, mean squared error, and Pearson correlation coefficient ρ results.…”

Section: Discussionmentioning

confidence: 99%

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Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation

Bryan

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The reverberation time (T60) and the direct-to-reverberant ratio (DRR) are commonly used to characterize room acoustic environments. Both parameters can be measured from an acoustic impulse response (AIR) or using blind estimation methods that perform estimation directly from speech. When neural networks are used for blind estimation, however, a large realistic dataset is needed, which is expensive and time consuming to collect. To address this, we propose an AIR augmentation method that can parametrically control the T60 and DRR, allowing us to expand a small dataset of real AIRs into a balanced dataset orders of magnitude larger. Using this method, we train a previously proposed convolutional neural network (CNN) and show we can outperform past single-channel state-of-the-art methods. We then propose a more efficient, straightforward baseline CNN that is 4-5x faster, which provides an additional improvement and is better or comparable to all previously reported single-and multi-channel state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

“…where h(t) is an AIR, t is a discrete time index, h e (t) is the early response, h l (t) is the late-field response, t d is the time delay of the direct path, and t 0 is tolerance window set to 2.5 ms [5]. We identify the location of the direct path as the time of the maximum of the absolute value of h(t).…”

Section: Impulse Response Augmentationmentioning

confidence: 99%

Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation

Bryan

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…1a appears to almost perfectly model the AIR when comparinĝ h(t) to h(t). The same process was repeated for a measured AIR, part of the Acoustic Characterization of Environments (ACE) Database [22]. The recording took place in a lecture room with dimensions [6.9, 9.7, 3.0] m and the receiver is one of the channels of a 32 microphone array.…”

Section: Methodsmentioning

confidence: 99%

“…For the second experiment the task was to model the 42 AIRs recorded using a 3 microphone mobile phone array in 7 rooms provided in the ACE database [22]. This corresponds to 2 sets of measurements, with the receiver positions varying between the two and the rest of the setup unchanged.…”

Section: Methodsmentioning

confidence: 99%

Sparse parametric modeling of the early part of acoustic impulse responses

Papayiannis

Evers

Naylor

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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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.

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“…We follow the methodology of Karjalainen et al [25] when computing the T 60 from real IRs with a measurable noise floor. This method was found to be the most robust estimator when computing the T 60 from real IRs in recent work [14]. The final composition of our dataset is listed in Table 2.…”

Section: Data Augmentationmentioning

confidence: 99%

Scene-Aware Audio Rendering via Deep Acoustic Analysis

Tang

Bryan

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Given a natural sound in a real-world room that is recorded using a cellphone microphone (left), we estimate the acoustic material properties and the frequency equalization of the room using a novel deep learning approach (middle). We use the estimated acoustic material properties for generating plausible sound effects in the virtual model of the room (right). Our approach is general and robust, and works well with commodity devices.Abstract-We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models. Given the captured audio and an approximate geometric model of a real-world room, we present a novel learning-based method to estimate its acoustic material properties. Our approach is based on deep neural networks that estimate the reverberation time and equalization of the room from recorded audio. These estimates are used to compute material properties related to room reverberation using a novel material optimization objective. We use the estimated acoustic material characteristics for audio rendering using interactive geometric sound propagation and highlight the performance on many real-world scenarios. We also perform a user study to evaluate the perceptual similarity between the recorded sounds and our rendered audio.

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Estimation of Room Acoustic Parameters: The ACE Challenge

Cited by 135 publications

References 27 publications

Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation

Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation

Sparse parametric modeling of the early part of acoustic impulse responses

Scene-Aware Audio Rendering via Deep Acoustic Analysis

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