The single- and multichannel audio recordings database (SMARD)

Nielsen, Jesper Kjær; Jensen, Jesper; Jensen, Søren Holdt; Christensen, Mads Græsbøll

doi:10.1109/iwaenc.2014.6953334

Cited by 43 publications

(38 citation statements)

References 14 publications

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“…Both simulated and real measured RIRs are included in this set. Four different databases are considered to build the real room impulse response set: MARDY [52]; REVERB challenge [53]; C4DM RIR [54]; and SMARD [55]. Only recordings from the B-format microphone taken in the Great Hall are considered within the C4DM RIR database due to an artefact in the other C4DM recordings at the 125 Hz octave band.…”

Section: B Data Setsmentioning

confidence: 99%

A Single-Channel Non-Intrusive C50 Estimator Correlated With Speech Recognition Performance

Parada

Sharma

Laínez

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Several intrusive measures of reverberation can be computed from measured and simulated room impulse responses, over the full frequency band or for each individual mel-frequency subband. It is initially shown that full-band clarity index C50 is the most correlated measure on average with reverberant speech recognition performance. This corroborates previous findings but now for the dataset to be used in this study. We extend the previous findings to show that C50 also exhibits the highest mutual information on average. Motivated by these extended findings, a non-intrusive room acoustic (NIRA) estimation method is proposed to estimate C50 from only the reverberant speech signal. The NIRA method is a data-driven approach based on computing a number of features from the speech signal and it employs these features to train a model used to perform the estimation. The choice of features and learning techniques are explored in this work using an evaluation set which comprises approximately 100000 different reverberant signals (around 93 hours of speech) including reverberation from measured and simulated room impulse responses. The feature importance of each feature with respect to the estimation of the target C50 is analysed following two different approaches. In both cases the newly chosen set of features shows high importance for the target. The best C50 estimator provides a root mean square deviation around 3 dB on average for all reverberant test environments.

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Section: B Data Setsmentioning

confidence: 99%

A Single-Channel Non-Intrusive C50 Estimator Correlated With Speech Recognition Performance

Parada

Sharma

Laínez

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The RIRs were taken from three publicly available databases, namely MARDY [45], SMARD [46], and MIRD [47]. A fourth database of 24 low-frequency RIRs, called SUBRIR [48], was used separately to evaluate the modeling performance of the algorithm in the modal frequency region, as will be discussed later in this section.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

Vairetti

Sena

Catrysse

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Parametric modeling of room acoustics aims at representing room transfer functions (RTFs) by means of digital filters and finds application in many acoustic signal enhancement algorithms. In previous work by other authors, the use of orthonormal basis functions (OBFs) for modeling room acoustics has been proposed. Some advantages of OBF models over all-zero and pole-zero models have been illustrated, mainly focusing on the fact that OBF models typically require less model parameters to provide the same model accuracy. In this paper, it is shown that the orthogonality of the OBF model brings several additional advantages, which can be exploited if a suitable algorithm for identifying the OBF model parameters is applied. Specifically, the orthogonality of OBF models does not only lead to improved model efficiency (as pointed out in previous work), but also leads to improved model scalability and model stability. Its appealing scalability property derives from a previously unexplored interpretation of the OBF model as an approximation to a solution of the inhomogeneous acoustic wave equation. Following this interpretation, a novel identification algorithm is proposed that takes advantage of the OBF model orthogonality to deliver efficient, scalable and stable OBF model estimates, which is not necessarily the case for nonlinear estimation techniques that are normally applied.

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“…outside the frequency range of the subwoofers. The code for generating the sweep signal and its inverse was adapted from the code provided in [3].…”

Section: The Exponential Sine-sweep (Ess) Measurement Technique: a Sumentioning

confidence: 99%

“…of post-processed RIRs, measured with subwoofer B and microphone D, for which a low-pass filter with cut-off frequency at 1 kHz and 100 Hz roll-off has been used, is available for download 3 . An example of the result of averaging is given in Figure 9, comparing the spectrogram and magnitude response of the RIR retrieved from a single recording 3 https://lirias.kuleuven.be/bitstream/123456789/572970/ 3/SUBRIR SpB MicD RIRs.zip (password: subrir2016) (top) and after synchronous averaging over 10 recordings (bottom), with source-receiver position pair (p, q) = (3, 5), and with subwoofer B and microphone D. From the magnitude responses, computed over the causal part of the RIR, it can be seen how averaging is able to reduce the noise level by at least 10 dB, including the very LF disturbance already noticed in Figure 7. From the spectrograms, it can be observed how the reduction in the noise level makes the nonlinear distortions more visible; the fact that the impulsive occurrences of the rub & buzz effect are not reduced in level after averaging, is a confirmation of the deterministic nature of these events.…”

Section: Post-processingmentioning

confidence: 99%

“…for applications such as dereverberation [1], source separation [2], source localization [3], blind acoustic parameter estimation [4], convolutive reverb [5], and many others. Several available RIR databases [1][2][3][4][5][6][7] are intended for different audio signal processing tasks, each requiring a different choice of measurement technique and of the measuring equipment. For instance, the databases in [6] and [7] contain binaural and head-related RIRs, and are useful in hearing-aids applications.…”

Section: Introductionmentioning

confidence: 99%

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The Subwoofer Room Impulse Response (SUBRIR) Database

Vairetti

Kaplanis²,

Sena

et al. 2017

J. Audio Eng. Soc.

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This report introduces a new database of room impulse responses (RIRs) measured in an empty rectangular room using subwoofers as sound sources. The purpose of this database, publicly available for download, is to provide acoustic measurements within the frequency region of modal resonances. Performing acoustic measurements at low frequencies presents many difficulties, mainly related to ambient noise and to unavoidable nonlinearities of the subwoofer. In this report, it is shown that these issues can be addressed and partially solved by means of the exponential sine-sweep technique and a careful calibration of the measurement equipment. A procedure for estimating the reverberation time at very low frequencies is proposed, which uses a cosine-modulated filterbank and an approximation of the RIRs using parametric models in order to reduce problems related to low signal-to-noise ratio and to the length of typical band-pass filter responses. [7] contain binaural and head-related RIRs, and are useful in hearing-aids applications. Other databases present specific configurations of the microphones, usually arranged into arrays. What is common to all these databases is that they use fullrange loudspeakers, whose frequency response typi-* To whom correspondence should be addressed. Tel: +32-16-321817; e-mail: giacomo.vairetti@esat.kuleuven.be cally has a lower bound of 50-100 Hz. While these databases cover a frequency range sufficient for the development and evaluation of speech enhancement algorithms, information about a significant portion of the modal response of the room is missing. INTRODUCTIONNowadays, home audio systems generally include a subwoofer, which is intended for the reproduction of low-frequency content typically in the region between 20 Hz and 150 Hz. In this frequency range, small-sized typical rooms operate within the modal frequency region [8]. In small-sized rooms, most of the acoustical problems are actually due to poor acoustics at very low frequencies (LFs). The modal resonances are usually well separated, energetic, and detectable by the human ear [9], thus degrading the perceived sound quality. A subwoofer with small enough lower cut-off frequency can even partially excite the so-called cavity mode (i.e. the modal resonance centered at 0 Hz). Therefore, algorithms for home audio system applications, such as room compensation algorithms, should be validated also on

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The single- and multichannel audio recordings database (SMARD)

Cited by 43 publications

References 14 publications

A Single-Channel Non-Intrusive C50 Estimator Correlated With Speech Recognition Performance

A Single-Channel Non-Intrusive C50 Estimator Correlated With Speech Recognition Performance

A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

The Subwoofer Room Impulse Response (SUBRIR) Database

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