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Dynamic Diffuse Signal Processing for Sound Reinforcement and Reproduction Dynamic diffuse signal processing for sound reinforcement and reproduction. Item Type Article Authors Moore, Jonathan B.; Hill, Adam J. Citation Moore, J.B., and Hill, A.J. (2018) ‘Dynamic diffuse signal processing for sound reinforcement and reproduction’, Journal of the Audio Engineering Society, 66 (11), pp.953-965. doi: 10.17743/ jaes.2018.0054 DOI 10.17743/jaes.2018.0054 Publisher Audio Engineering Society Journal Journal of the Audio Engineering Society Download date 28/09/2021 22:19:18 Link to Item http://hdl.handle.net/10545/623149 PAPERS Dynamic diffuse signal processing for sound reinforcement and reproduction Jonathan B. Moore, AES Member, Adam J. Hill, AES Member [email protected] [email protected] Department of Electronics, Computing and Mathematics, University of Derby, Derby, DE22 1GB, United Kingdom High inter-channel coherence between signals emitted from multiple loudspeakers can cause undesirable acoustic and psychoacoustic effects. Examples include position-dependent low- frequency magnitude response variation, where comb-filtering leads to the attenuation of certain frequencies dependent on path length differences between multiple coherent sources, lack of apparent source width in multi-channel reproduction and lack of externalization in headphone reproduction. This work examines a time-variant, real-time decorrelation algorithm for the reduction of coherence between sources as well as between direct sound and early reflections, with a focus on minimization of low-frequency magnitude response variation. The algorithm is applicable to a wide range of sound reinforcement and reproduction applications, including those requiring full-band decorrelation. Key variables which control the balance between decorrelation and processing artifacts such as transient smearing are described and evaluated using a MUSHRA test. Variable values which render the processing transparent whilst still providing decorrelation are discussed. Additionally, the benefit of transient preservation is investigated and is shown to increase transparency. 0 INTRODUCTION multiple of f0 will also be subject to similar cancellation. This gives rise to the well-known comb-filtering effect. In many sound reinforcement and reproduction When TDOA is small, comb-filtering is limited to mid- scenarios, the desired audience sound coverage may only and high-frequencies. Subjectively, this is experienced as be achieved by using multiple electro-acoustic transducers timbral anomalies between the received and source emitting coherent signals at equal or nearly equal sound signals. For large-scale sound reinforcement systems, power levels. Where transducers are not arrayed in such a path-length differences are regularly on the order of way that leads to acoustical coupling over their operational several meters, leading to comb-filtering commencing at frequency range, any difference in path-length from a low-frequencies. In this case, there will exist frequency- listening position or acoustic measurement point to two or dependent amplitude nulls spanning several meters. The more loudspeakers will give a relative phase difference overall subjective implication is that audience members between the emitted signals [1]. The summation of such will receive a magnitude response that both differs from signals will result in a frequency response that is dependent the source material and is position-dependent. on the path-length differences, and cancellation of Spatial variance quantifies the magnitude response frequencies where phase difference equates to 180 variation over a pre-determined frequency range and degrees. audience area [2,3,4], given by Eq. 0.2. For a two-transducer system, the fundamental frequency of cancellation is given by Eq. 0.1. 푓ℎ 푁푝 1 1 1 SV = ∑ √ ∑(퐿 (푝, 푖) − 퐿 (푖))2 0.2 푓0 = 0.1 푝 푝 2푡 푁푓 푁푝 − 1 푖=푓 푝=1 푙표 where, f0 is the fundamental frequency of cancellation (Hz) and t is the time difference of arrival (TDOA) between the where, SV is spatial variance in (dB), calculated based on two transducers (s). the number of frequency bins (Nf), the number of Therefore, f0 is inversely proportional to TDOA, measurement points (Np), the frequency range of interest meaning that greater TDOA causes lower a fundamental (flo to fhi, in Hz), the sound pressure level (dB) at frequency of cancellation. Additionally, any integer measurement point p and frequency bin i, Lp(p,i), and the Journal information 1 Moore and Hill Diffuse signal processing for sound reinforcement and reproduction mean sound pressure level (dB) over all measurement width [13], increased headphone externalization [29] and synthetic reverb [19]. Early algorithms were primarily points at frequency bin i, Lp(i) . used to produce a pseudo-stereophonic signal from a As described by Eq. 0.2, SV is the standard deviation of monophonic source. These methods rely on the generation sound pressure level for all frequency bins of interest of complimentary comb-filters by use of either delay lines across all measurement points. 0 dB SV implies no or all-pass filters [6, 7, 8]. Using this method, two deviation in magnitude response across a listening area, independent signals may be generated whose summed disregarding propagation loss if the responses are magnitude response is proportional to the magnitude normalized. response of the input signal. However, these methods are A potential solution to high SV across an audience is to not suitable for the applications discussed in this work reduce inter-channel drive-signal coherence using since only a limited number of sources may be decorrelation. In this case, the acoustic signals will sum by decorrelated using this technique and perfect summation is the powers of their amplitudes since phase is randomized only achieved in a limited sweet-spot [9, 10]. [5]. Should sufficient signal decorrelation be achieved, Other decorrelation methods have been developed for interference effects will be minimized and the resulting use in stereophonic echo-cancellation for voice sound field will be approximately diffuse, characterized by conferencing [11, 12]. Unfortunately, these are also a consistent magnitude response across an audience. unsuitable for the specific sound reinforcement and A signal decorrelation algorithm termed diffuse signal reproduction applications in this research due to the level processing (DiSP), first described in [22], has been of distortion introduced and the limited number of investigated in prior work by the authors [23, 24, 25]. It decorrelated signals generated [13]. was found to be a useful tool for the decorrelation of Kendall [14] describes a method of decorrelation filter multiple sources in sound reinforcement and reproduction generation, whereby filter coefficients are obtained via an applications. However, in [24] simulations showed that inverse Fourier transform of a frequency domain DiSP performance is reduced in closed acoustic spaces specification of unity magnitude and random phase. This when decorrelation filters remain fixed. This is because method allows for the generation of a large number of direct sources maintain coherence with their early decorrelation filters which display low correlation with reflections, leading to comb filtering. each other, allowing for many discrete sources to be Therefore, a time-varying DiSP algorithm was decorrelated. However, it was found that while unity introduced in [24], termed dynamic DiSP. This work magnitude and random phase are specified at each advances dynamic DiSP with the introduction of two key frequency bin, the resulting magnitude spectrum from the user definable variables that may be used to balance inverse Fourier transform is not uniform in between these dynamic decorrelation performance with processing points leading to timbral coloration [14, 15, 16]. perceptibility. Decorrelation has also been achieved with frequency A MUSHRA style subjective test is presented to suggest domain processing, where the signal is passed through a suitable limits for these variables and to assess the filter bank to divide the signal into critical frequency transparency of the algorithm in comparison to bands, with a random time shift applied to each band [15, unprocessed musical samples. Further to this, transient 17, 18]. Depending on the magnitude of the random time extraction prior to dynamic DiSP processing is utilized and shift calculated per frequency band, this method may result its impact on decorrelation versus perceptual transparency in frequency cancellation at band edges when the signal is is investigated. reconstructed [15, 16]. This occurs when the time shifts After a brief review of existing signal decorrelation equate to approximately 180 degrees phase difference methods in Section 1, DiSP is reviewed in Section 2, between edge frequencies. This may be alleviated by followed by a justification for the need for a time-varying, constraining the time shift per band to multiples of 360 dynamic variant of DiSP, capable of direct signal and early degrees phase shift for the edge frequency of each band reflection decorrelation. Transient detection for the [15]. However, this then limits the number of discrete preservation of input signal’s sharp transient content is sources which may be decorrelated. There will be a limited also investigated (Section 3). The algorithm is objectively number of time shift values for each band that meet the analyzed in Section 4 using image-source modeling. This criteria of being sufficient for decorrelation, not exceeding is followed
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