# Signal Processing Implementation and Comparison of Automotive Spatial Sound Rendering Strategies

- MingsianR Bai
^{1}Email author and - Jhih-Ren Hong
^{1}

**2009**:876297

https://doi.org/10.1155/2009/876297

© M. R. Bai and J.-R. Hong. 2009

**Received: **9 September 2008

**Accepted: **8 June 2009

**Published: **24 August 2009

## Abstract

Design and implementation strategies of spatial sound rendering are investigated in this paper for automotive scenarios. Six design methods are implemented for various rendering modes with different number of passengers. Specifically, the downmixing algorithms aimed at balancing the front and back reproductions are developed for the 5.1-channel input. Other five algorithms based on inverse filtering are implemented in two approaches. The first approach utilizes binaural (Head-Related Transfer Functions HRTFs) measured in the car interior, whereas the second approach named the point-receiver model targets a point receiver positioned at the center of the passenger's head. The proposed processing algorithms were compared via objective and subjective experiments under various listening conditions. Test data were processed by the multivariate analysis of variance (MANOVA) method and the least significant difference (Fisher's LSD) method as a *post hoc* test to justify the statistical significance of the experimental data. The results indicate that inverse filtering algorithms are preferred for the single passenger mode. For the multipassenger mode, however, downmixing algorithms generally outperformed the other processing techniques.

## 1. Introduction

With rapid growth in digital telecommunication and display technologies, multimedia audiovisual presentation has become reality for automobiles. However, there remain numerous challenges in automotive audio reproduction due to the notorious nature of the automotive listening environment. In car interior, the confined space lacks natural reverberations. This may degrade the perceived spaciousness of audio rendering. Localization of sound images may also be obscured by strong reflections from the window panels, dashboard, and seats [1]. In addition, the loudspeakers and seats are generally not in proper positions and orientations, which may further aggravate the rendering performance [2, 3]. To address these problems, a comprehensive study of automotive multichannel audio rendering strategies is undertaken in this paper. Rendering approaches for different numbers of passengers are presented and compared.

In spatial sound rendering, binaural audio lends itself to an emerging audio technology with many promising applications [4–10]. It proves effective in recreating stereo images by compensating for the asymmetric positions of loudspeakers in car environment [1]. However, this approach suffers from the problem of the limited "sweet spot" in which the system remains effective [7, 8]. To overcome this limitation, several methods that allow for more accurate spatial sound field synthesis were suggested in the past. The Ambisonics technique originally proposed by Gerzon is a series of recording and replay techniques using multichannel mixing technology that can be used live or in the studio [11]. The Wave Field Synthesis (WFS) technique is another promising method to creating a sweet-spot-free rendering environment [12–14]. Nevertheless, the requirement of large number of loudspeakers, and hence the high processing complexity, limits its implementation in practical systems.

Notwithstanding the eager quest for advanced rendering methods in academia, the majority of the off-the-shelf automotive audio systems still rely on simple systems with panning and equalization functions. For instance, Pioneer's (Multi-Channel Acoustic Calibration MCACC) system attempts to compensate for the acoustical responses between the listener's head position and the loudspeaker by using a 9-band equalizer [15]. Rarely has been seen a theoretical treatment with rigorous evaluation on the approaches that have been developed for this difficult problem.

If binaural audio and the WFS are regarded as two extremes in terms of loudspeaker channels, this paper is focused on pragmatic and compromising approaches of automotive audio spatializers targeted at economical cars with four available loudspeakers for 5.1-channel input contents. In these approaches, it is necessary to downmix the audio signals to decrease the number of audio channels between the inputs and the outputs [16]. By combining various inverse filtering and the downmixing techniques, six rendering strategies are proposed for various passengers' sitting modes. One of the six methods is based on downmixing approaches, whereas the remaining five methods are based on inverse filtering.

The proposed approaches have been implemented on a real car by using a fixed-point digital signal processor (DSP). Extensive objective and subjective experiments were conducted to compare the presented rendering strategies for various listening scenarios. In order to justify the statistical significance of the results, the data of subjective listening tests are processed by the multivariate analysis of variance (MANOVA) [17] method, followed by the least significant difference method (Fisher's LSD) as a *post hoc* test. In light of these tests, it is hoped that viable rendering strategies capable of delivering compelling and immersive listening experience in automotive environments can be found.

## 2. Downmixing-Based Strategy

Next, the frontal channels are weighted (0.65) and delayed (20 millisecond) to produce the back channels.

## 3. Inverse Filtering-Based Approaches

Beside the aforementioned downmixing-based strategy, five other strategies are based on inverse filtering. These design strategies are further divided into two categories. The first category is based on the Head-Related Transfer Functions (HRTFs) that account for the diffraction and shadowing effects due to the head, ears, and torso. Three rendering strategies are developed to reproduce four virtual images located at and in accordance with the 5.1 deployment stated in ITU-R Rec. BS.775-1 [18]. For the 5.1-channel inputs and four loudspeakers, the center channel has to be attenuated by 3 dB and mixing into the front-left and the front-right channels. The HRTF database measured by the MIT Media Laboratory [19, 20] is employed as the matching model, whereas the HRTFs measured in the car are used as the acoustical plant. The second category named "the point-receiver model" regards the passenger's head as a simple point-receiver at the center.

### 3.1. Multichannel Inverse Filtering

where symbolizes the Frobenius norm [21]. Using Tikhnov regularization, the inverse filter matrix can be shown to be [7].

The regularization parameter *β* that weights the input power against the performance error can be used to prevent the singularity of
**H** from saturating the filters. If
is too small, there will be sharp peaks in the frequency responses of the CCS filters, whereas if
is too large, the cancellation performance will be rather poor. The criterion for choosing the regularization parameter
is dependent on a preset gain threshold [7]. Inverse Fast Fourier transforms (FFT) along with circular shifts (hence the modeling delay) are needed to obtain causal FIR filters.

In general, it is not robust to implement the inverse filters based on the measured room responses that usually have many noninvertible zeros (deep troughs) [22]. In this paper, a generalized complex smoothing technique suggested by Hatziantoniou and Mourjopoulos [23] is employed to smooth out the peaks and dips of the acoustical frequency responses before the design of inverse filters.

### 3.2. Inverse Filtering-Based Approaches and Formulation

#### 3.2.1. HRTF Model

**H**and the 2 4 matching model matrix can be written as

where the superscripts
and
refer to the ipsilateral and the contralateral paths, respectively. The subscripts 30 and 110 in the matching model matrix
signify the azimuth angles of the HRTF. The HRTFs are assumed to use symmetry, the
and
are generated by swapping the ipsilateral and contralateral sides of
and
. The acoustical plants **H**
are the frequency response functions between the inputs to the loudspeakers and the outputs from the microphones mounted in the (Knowles Electronics Manikin for Acoustic Research KEMAR's) [19, 20] ears. This leads to a 4
4 matrix inversion problem, which is computationally demanding to solve. In order to yield a more tractable solution, the current research has separated this problem into two parts: the front side and the back side. Specifically, the frontal loudspeakers are responsible for generating the sound images at
, while the back loudspeakers are responsible for generating the sound images at
. In this approach, the plant, the matching model, and the inverse filter matrices are given by

where superscripts and denote the front-side and the back-side, respectively. The inverse matrices are calculated using (3). In comparison with the formulation in (4) and (5), a great saving of computation can be attained by applying this approach. The number of the inverse filters reduces from sixteen (one 4 4 matrix) to eight (two 2 2 matrices).

To be specific, there are two –one for the ipsilateral side ( ) and another for contralateral side ( ). Both HRTFs refer to the transfer functions between a source positioned at with respect to the head center and two ears. Although the loudspeakers in the car are not symmetrically deployed, the matching model (consisting of and ) of the inverse filter design in the present study is chosen tom be symmetrical. For the asymmetrical acoustical plants, we can calculate the inverse filters using (3). The loudspeaker setups are not symmetrical for the front left virtual sound and the front right virtual sound and hence the acoustical plants are not symmetrical. This results in different solutions for the inverse filters.

Next, the situation with two passengers sitting on different seats, for example, the front left and the back right seats, is examined. This problem involves four control points for two passengers' ears, four loudspeakers, and four input channels. Following the steps from the single passenger case, the design of the inverse filter can be divided into two parts. Accordingly, two 4 2 matrices of the acoustical plants, two 4 2 matrices of the matching models, and two 2 2 matrices of the inverse filters are expressed as follows:

The subscripts of
*z*), are as follows *i* = 1,2 refers to the left and right ears of the passenger 1,
= 3,4 refers to the left and the right ears of the passenger 2, and
= 1,2,3,4 refers to the four loudspeakers. In the 4
2 matrices
(*z*) and
(*z*), the first and second rows are identical to the third and fourth rows. Specifically, the rows 1 and 2 are for passenger 1 while the rows 3 and 4 are for passenger 2. The two HRTF inversion methods outlined in (6)–(8) and (9)–(11) were used to generate the following test.

HRTF-Based Inverse Filtering for Single Passenger

**H**(

*z*) in (6) are measured. The matching model matrices and the inverse filters are given in (7) and (8). The weight = 0.45 and delay = 4 ms are used in mixing the four-channel inputs into the respective channels. It is noted that this procedure will also be applied to the following inverse-filtering-based methods.

HRTF-Based Inverse Filtering (HIF2) for Two Passengers

In this section, two HRTF-based inverse filtering strategies designed for two passengers and 5.1-channel input are presented. The first approach named the HIF2 method considers four control points for two passengers. The associated system matrices take the form formulated in (9) to (11). The two 2 2 inverse filter matrices are calculated as previously. The block diagram of the HIF2 method follows that of the HIF1 method.

HRTF-Based Inverse Filtering (HIF2-S) for Two Passengers

This approach is named the HIF2-S method. In (12), the design procedures of the HIF2-S method are divided into two steps. First, the inverse filters for a single passenger sitting on respective positions are designed. Next, by adding the filter coefficients obtained in the first step, two 2 2 inverse filter matrices are obtained. The block diagram of the HIF2-S method follows that of the HIF1 method.

#### 3.2.2. Point-Receiver Model

where denotes the transfer function from the th loudspeaker to the control point. The frequency response function measured using the same type of loudspeakers in the car in an anechoic chamber is designated as the matching model . The point-receiver model was used to generate the following test system.

Point-Receiver-Based Inverse Filtering for Single Passenger

Point-Receiver-Based Inverse Filtering for Two Passengers

For the rendering scenario with two passengers and 5.1-channel input, the aforementioned filter superposition idea is employed in the point-receiver-based inverse filtering approach (PIF2-S). The structure of this rendering approach is similar to those of the PIF1 approach, as shown in Figure 6. A PIF2 system analogous to the HIF2 system was considered in initial tests, but was eliminated from final testing because the PIF2 approach performed badly in an informal experiment, as compared with the other approaches.

## 4. Objective and Subjective Evaluations

The descriptions of ten automotive audio rendering approaches.

Method | No. input channel | No. passenger | Design strategy |
---|---|---|---|

DWD | 5.1 | 1 or more | Downmixing + weighting & delay |

HIF1 | 5.1 | 1 | HRTF-based inverse filtering |

HIF2 | 5.1 | 2 | HRTF-based inverse filtering |

HIF2-S | 5.1 | 2 | HRTF-based inverse filtering |

PIF1 | 5.1 | 1 | Point-receiver-based inverse filtering |

PIF2-S | 5.1 | 2 | Point-receiver-based inverse filtering |

### 4.1. Objective Experiments

#### 4.1.1. The HRTF-Based Model

**H**( )

**C**( ). The agreement between these two sets of responses is generally good below 6 kHz except for the back loudspeaker. This is because the inverse filters are gain-limited in the frequencies at which the plants have significant roll-off.

**H**. A further comparison of the HIF2 and HIF2-S methods will be presented in the following subjective tests.

#### 4.1.2. The Point-Receiver-Based Model

*H*( )

*C*( ). The responses are generally in good agreement below 10 kHz except for the back loudspeakers.

### 4.2. Subjective Experiments

*localization*.

*preference*, the timbral attributes (

*fullness*,

*brightness*,

*artifact*) and the spatial attributes (

*localization*,

*frontal image*,

*proximity*,

*envelopment*) are summarized in Table 3. Forty subjects participating in the listening tests were instructed with definitions of the subjective attributes and the procedures before the tests. The subjects were asked to respond in a questionnaire after listening, with the aid of a set of subjective attributes measured on an integer scale from 3 to 3. Positive, zero, and negative scores indicate perceptually improvement, no difference, and degradation, respectively, of the signals processed by the rendering algorithm under test. The order to grade the attributes is randomized except that the attribute

*preference*is always graded last. In order to access statistical significance of the test results, the scores were further processed by using the MANOVA. If the significance level is below 0.05, the difference among all methods is considered statistically significant and will be processed further by the Fisher's LSD

*post hoc*test to perform multiple paired comparisons.

The definitions of the subjective attributes.

Attribute | Description |
---|---|

Preference | Overall preference in considering timbral and spatial attributes |

Fullness | Dominance of low-frequency sound |

Brightness | Dominance of high-frequency sound |

Artifacts | Any extraneous disturbances to the signal |

Localization | Determination by a subject of the apparent source direction |

Frontal | The clarity of the frontal image or the phantom center |

Proximity | The sound is dominated by the loudspeaker closest to the subject |

Envelopment | Perceived quality of listening within a reverberant environment |

#### 4.2.1. Experiment I

Methods

Experiment I is intended for evaluating the rendering algorithms designed for one passenger in the FL seat or BR seat. The DWD, HIF1, and PIF1 methods are compared in this experiment. Because only four loudspeakers are available in this car, the center channel of the 5.1-channel input is attenuated by 3 dB and mixed into the frontal channels to serve as the hidden reference. In addition, the four channels of input signals are summed and lowpass filtered (with 4 kHz cutoff frequency) to serve as the anchor.

Results

Figures 13(a) and 13(b) show the means and spreads of the grades on the subjective attributes for the FL position, while Figures 13(c) and 13(d) show the results for the BR position. For the FL position, the results of the *post hoc* test indicate that the grades of the HIF1 method in *preference* and *fullness* are significantly higher than those of the DWD and the PIF1 methods. In *brightness*, only the grade of PIF1 methods is significantly higher than the hidden reference, while no significant difference between the DWD method and the HIF1 method is found. In addition, there is no significant difference among methods in the attributes *artifact*, *localization*, *proximity* and *envelopment*. In the attribute *frontal*, however, the inverse filter-based methods received significantly higher grades than the hidden reference and the DWD method.

*fullness*,

*artifact,*and

*localization*. However, the grades received in

*preference*and

*brightness*using the inverse filtering-based method is significantly higher than the grades obtained using the other methods. In addition, all rendering methods received significantly higher grades in

*proximity*than the hidden reference. Finally, only the HIF1 method significantly outperformed the hidden reference in

*envelopment*. In general, all grades received are higher for the back seat than for the front seat. The HIF1 method received the highest grades in most attributes, especially in the spatial attributes. Considering the computation complexity, the PIF1 method is also a viable approach second to the HIF1 method because it received high grades in many attributes as well.

#### 4.2.2. Experiment II

Methods

Experiment II is intended for evaluating the rendering algorithms designed for two passengers in the FL seat and BR seat and the 5.1-channel input. Four methods including the DWD method, the HIF2 method, the HIF2-S method, and the PIF2-S method are compared in this experiment. The hidden reference and the anchor are identical to those defined in Experiment I.

Results

*post hoc*test reveals that there is no significant difference between the DWD method and HIF2-S method, while both grades in

*preference*are significantly higher than the hidden reference. In

*fullness*and

*proximity*, no significant difference was found among all proposed methods. In

*brightness*, results similar to Experiment I are obtained. The inverse filtering-based methods received significant higher grades than the hidden reference, albeit there is no significant difference among the inverse filtering-based methods methods. The HIF2 method received very low grade in

*artifact*, implying that artifacts are audible. This could be due to the problem of inverse filter design for the nonsquare acoustical system. In

*frontal*and

*localization*, all methods received significantly higher grades than the hidden reference. Finally, the HIF2-S method has attained the best performance in

*envelopment*among all methods. Overall, the HIF2-S method is the preferred choice for spatial quality, which is contrary to our expectation that more inverse filters (HIF2) should yield better performance. On the other hand, in terms of computation complexity and rendering performance, the DWD method is an adequate choice for the two-passenger scenario.

## 5. Conclusions

The summary of the rendering strategies recommended for various listening scenarios.

Passenger | Number input channel | Strategy | |
---|---|---|---|

1 | FL | 4 | HIF1 |

1 | BR | 4 | PIF1 |

2 | 4 | DWD |

First, for the rendering scenario with a single passenger and the 5.1-channel inputs, the HIF1 method is suggested for the passenger sitting in the FL seat, whereas the PIF1 method would be the preferred choice for the passenger sitting in the BR seat. Second, for the two-passenger scenario, the HIF2-S method received high grade in most subjective attributes. However, no significant difference in the attributes *preference, brightness, artifact, localization* and *frontal* was found between the DWD method and the HIF2-S method. Considering the computational complexity, the DWD method should be the most preferred choice for the two-passenger scenario. Overall, the inverse filtering approaches did not perform as well for the multipassenger scenario as it did for the single passenger scenario. The number of inverse filters increases drastically with number of passengers, rendering approaches of this kind impractical in automotive applications.

## Declarations

### Acknowledgments

The work was supported by the National Science Council in Taiwan, China, under the project no. NSC91-2212-E009-032.

## Authors’ Affiliations

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