### Spatial Audio Reproduction with Primary Ambient Extraction

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Gan, and E. Letters, vol. Merimaa, M. Goodwin, and J. Gan and E.

### Informed Hybrid Primary Ambient Extraction for Spatial Audio Reproduction

Choi and S. Ko Sch. Brian D. Huggins 6 December Mark Allie comp. Similar presentations. Upload Log in. My presentations Profile Feedback Log out. Log in. Auth with social network: Registration Forgot your password? Download presentation. Cancel Download. Presentation is loading. Please wait. Copy to clipboard. About project SlidePlayer Terms of Service. Feedback Privacy Policy Feedback. All rights reserved. To make this website work, we log user data and share it with processors. A variety of new spatial audio algorithms have been enabled by this robust and flexible parameterization of audio scenes, which we refer to hereafter as spatial audio scene coding SASC ; for example, this spatial parameterization has been used for high-fidelity conversion between arbitrary multichannel audio formats.

In this architecture, the SASC spatial analysis is used to determine the perceived direction of each time-frequency component in the input audio scene. Then, each such component is rendered with the appropriate binaural processing for virtualization at that direction; this binaural spatial synthesis is discussed in the following section. Although the analysis was described above based on an STFT representation of the input signals, the SASC method can be equally applied to other frequency-domain transforms and subband signal representations.

Furthermore, it is straightforward to extend the analysis and synthesis to include elevation in addition to the azimuth and radial positional information. The processing steps in the synthesis algorithm are as follows and are carried out for each frequency bin k at each time l:.

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After the binaural signals are generated for all k for a given frame l, time-domain signals for presentation to the listener are generated by an inverse transform and overlap-add as shown in FIG. Input signals are converted to the frequency domain representation , preferably but not necessarily using a Short Term Fourier Transform The frequency-domain signals are preferably analyzed in spatial analysis block to generate at least a directional vector for each time-frequency component.

It should be understood that embodiments of the present invention are not limited to methods where spatial analysis is performed, or, even in method embodiments where spatial analysis is performed, to a particular spatial analysis technique. One preferred method for spatial analysis is described in further detail in copending application Ser.

Next, the time-frequency signal representation frequency-domain representation is further processed in the high resolution virtualization block This block achieves a virtualization effect for the selected output format channels by generating at least first and second frequency domain signals from the time frequency signal representation that, for each time and frequency component, have inter-channel amplitude and phase differences that characterize the direction that corresponds to the directional vector The first and second frequency domain channels are then converted to the time domain, preferably by using an inverse Short Term Fourier Transform along with conventional overlap and add techniques to yield the output format channels In the formulation of Equations 25, 26 , each time frequency component X m [k,l] is independently virtualized by the HRTFs.

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It is straightforward to manipulate the final synthesis expressions given in Equations 27, 28 to yield. Since undesirable signal cancellation can occur in the downmix, a normalization is introduced in a preferred embodiment of the invention to ensure that the power of the downmix matches that of the multichannel input signal at each time and frequency. The frequency-domain multiplications by F L [k,l] and F R [k,l] correspond to filtering operations, but here, as opposed to the cases discussed earlier, the filter impulse responses are of length K; due to the nonlinear construction of the filters in the frequency domain based on the different spatial analysis results for different frequency bins , the lengths of the corresponding filter impulse responses are not constrained.

Thus, the frequency-domain multiplication by filters constructed in this way always introduces some time-domain aliasing since the filter length and the DFT size are equal, i.

Listening tests indicate that this aliasing is inaudible and thus not problematic, but, if desired, it could be reduced by time-limiting the filters H L [k,l] and H R [k,l] at each time l, e. This convolution can be implemented approximately as a simple spectral smoothing operation to save computation. In either case, the time-limiting spectral correction alters the filters H L [k,l] and H R [k,l] at each bin k and therefore reduces the accuracy of the resulting spatial synthesis.

## Applying Primary Ambient Extraction for Immersive Spatial Audio Reproduction - Dimensions

This problem is also encountered in interactive 3-D positional audio systems. In one embodiment, the magnitude or minimum-phase component of H L [k,l] and H R [k,l] is derived by spatial interpolation at each frequency from a database of HRTF measurements obtained at a set of discrete directions. A simple linear interpolation is usually sufficient. The ITD is reconstructed separately either by a similar interpolation from measured ITD values or by an approximate formula. For instance, the spherical head model with diametrically opposite ears and radius b yields.

This separate interpolation or computation of the ITD is critical for high-fidelity virtualization at arbitrary directions. In a preferred embodiment:. For broadband transient events, the introduction of a phase modification in the DFT spectrum can lead to undesirable artifacts such as temporal smearing. Two provisions are effective to counteract this problem. First, a low cutoff can be introduced for the ITD processing, such that high-frequency signal structures are not subject to the ITD phase modification; this has relatively little impact on the spatial effect since ITD cues are most important for localization or virtualization at mid-range frequencies.

Second, a transient detector can be incorporated; if a frame contains a broadband transient, the phase modification can be changed from a per-bin phase shift to a broadband delay such that the appropriate ITD is realized for the transient structure. This assumes the use of sufficient oversampling in the DFT to allow for such a signal delay. Furthermore, the broadband delay can be confined to the bins exhibiting the most transient behaviorâ€”such that the high-resolution virtualization is maintained for stationary sources that persist during the transient.

A more general solution is defined as any 3-D encoding surface that preserves symmetry around the vertical axis and includes the circumference of the unit circle as its edge. An approximate near-field HRTF correction can be obtained by appropriately adjusting the interaural level difference for laterally localized sound sources.

In synthesizing complex audio scenes, different rendering approaches are needed for discrete sources and diffuse sounds; discrete or primary sounds should be rendered with as much spatialization accuracy as possible, while diffuse or ambient sounds should be rendered in such a way as to preserve or enhance the sense of spaciousness associated with ambient sources. For that reason, the SASC scheme for binaural rendering is extended here to include a primary-ambient signal decomposition as a front-end operation, as shown in FIG.

This primary-ambient decomposition separates each input signal X m [k,l] into a primary signal P m [k,l] and an ambience signal A m [k,l]; several methods for such decomposition have been proposed in the literature. Initially, the frequency domain input signals are processed in primary-ambient decomposition block to yield primary components and ambient components In this embodiment, spatial analysis is performed on the primary components to yield a directional vector Preferably, the spatial analysis is performed in accordance with the methods described in copending application, U.

Alternatively, the spatial analysis is performed by any suitable technique that generates a directional vector from input signals. Next, the primary component signals are processed in high resolution virtualization block , in conjunction with the directional vector information to generate frequency domain signals that, for each time and frequency component, have inter-channel amplitude and phase differences that characterize the direction that corresponds to the directional vector Ambience virtualization of the ambience components takes place in the ambience virtualization block to generate virtualized ambience components , also a frequency domain signal.

Since undesirable signal cancellation can occur in a downmix, relative normalization is introduced in a preferred embodiment of the invention to ensure that the power of the downmix matches that of the multichannel input signal at each time and frequency. The signals and are then combined. After the primary-ambient separation, virtualization is carried out independently on the primary and ambient components.

The spatial analysis and synthesis scheme described previously is applied to the primary components P m [k,l]. The ambient components A m [k,l], on the other hand, may be suitably rendered by the standard multichannel virtualization method described earlier, especially if the input signal is a multichannel surround recording, e. In the case of a two-channel recording, it is desirable to virtualize the ambient signal components as a surrounding sound field rather than by direct reproduction through a pair of virtual frontal loudspeakers.

In one embodiment, the ambient signal components A L [k,l] and A R [k,l] are directly added into the binaural output signal Y L [k,l] and Y R [k,l] without modification, or with some decorrelation filtering for an enhanced effect. This ambient upmixing process preferably includes applying decorrelating filters to the synthetic surround ambience signals.

The proposed SASC-based rendering method has obvious applications in a variety of consumer electronic devices where improved headphone reproduction of music or movie soundtracks is desired, either in the home or in mobile scenarios. The combination of the spatial analysis method described in U. The resulting listening experience is a closer approximation of the experience of listening to a true binaural recording of the recorded sound scene or of a given loudspeaker reproduction system in an established listening room.

enter Furthermore, unlike a conventional binaural recording, this reproduction technique readily supports head-tracking compensation because it allows simulating a rotation of the sound scene with respect to the listener, as described below. While not intended to limit the scope of the present invention, several additional applications of the invention are described below. The SASC-based binaural rendering embodiments described herein are particularly efficient if the input signal is already provided in the frequency domain, and even more so if it is composed of more than two channelsâ€”since the virtualization then has the effect of reducing the number of channels requiring an inverse transform for conversion to the time domain.

As a common example of this computationally favorable situation, the input signals in standard audio coding schemes are provided to the decoder in a frequency-domain representation; similarly, this situation occurs in the binaural rendering of a multichannel signal represented in a spatial audio coding format. The spatial synthesis methods described above thus form the core of a computationally efficient and perceptually accurate headphone decoder for the SASC format. The SASC-based binaural rendering method can be applied to other audio content than standard discrete multichannel recordings.

For instance, it can be used with ambisonic-encoded or matrix-encoded material. Similarly, it can be readily combined with the SIRR or DirAC techniques for high-resolution reproduction of ambisonic recordings over headphones or for the conversion of room impulse responses from an ambisonic format to a binaural format. The SASC-based binaural rendering method has many applications beyond the initial motivation of improved headphone listening. For instance, the use of the SASC analysis framework to parameterize the spatial aspects of the original content enables flexible and robust modification of the rendered scene.

Given that spatial separation is well known to be an important factor in speech intelligibility, such spatial widening may prove useful in improving the listening assistance provided by hearing aids.