VOLUME CONTROL IN NETWORKED AUDIO SYSTEMS

Aki Harma, Arno van Leest, and Rainer Thaden

aki.harma@philips.comPhilips Research Laboratories, Eindhoven, The Netherlands.

ABSTRACT

If all audio appliances in a building are connected to a networkand equipped with microphones it becomes feasible to consideran active volume control system where, e.g., audio played fromone device does not disturb other people in the neighborhood.The fundamental problems are the identification of the audiosource and the estimation of the acoustic transfer function. Forthe identification problem we propose a method where a uniqueimperceivable watermark is embedded in each loudspeaker sig-nal. For the path estimation we compare two algorithms in a sim-ulation and find that a simple method based on subband leveldifferences may be sufficient for many typical cases.

1. INTRODUCTION

Thethoughtless use ofaudio appliances is one of the largestcauses of noise complaints [1]. The reason for playing au-dio too loud is not always the lack of thoughtfulness, butrather the absence of practical means of knowing, for ex-ample, how loud the music played in a living room is per-ceived in the bedroom, or in neighbor’s bedroom. For thesame reason many people also set the volume inconve-niently low. In an ideal volume control the audio systemwould know how loud it really is at different locationsin the environment. Naturally one could perform a seriesof acoustic measurements in the dwelling to find out theacoustic transfer functionsHAB(ω) from each speakerAto each point of interestB. Then the transfer functionscould be used for dynamic equalization and level controlwith the help of a computational loudness model [2]. Sys-tems where a networked audio appliances are calibratedby off-line measurements have been proposed recently byseveral authors, e.g., [3]. In this paper we want to avoid theoff-line measurement and perform the path identificationduring the normal operation of the system.A vision that is endorsed by all electronics industries pre-dicts that all appliances at home will be connected to ahome network. Moreover, appliances are becoming increas-ingly audio-capablesuch that many devices in our envi-ronment have (built-in) electro-acoustic transducers suchas microphones or loudspeakers. A text book example ofan UPnP device is a toaster with a built-in mp3-player anda small speaker [4]. The standard home networking pro-

Figure 1:A networked audio system of two devices.

tocols such as UPnP makes it possible for the devices todiscovereach other, share information, and transfer mediastreams. If networked devices are also equipped with mi-crophones then there are various possibilities to measureacoustic characteristics of the environment.

2. ONLINE MEASUREMENTS IN ANETWORKED AUDIO SYSTEM

A typical setup considered in this article is the following.User A is listening to music in the living room from homestereos. The aim is to adjust the playback volume suchthat it is sufficiently high for User A, but at the same timedoes not disturb User B going to sleep in a bedroom. Weassume that User B has, e.g., a network-connected clockradio which has a built-in microphone. Clearly, the acous-tic pathH(ω) from the stereos to the clock radio can beestimated by the comparison of the microphone signal tothe original music signal available over the network. Forvolume control it is sufficient that the attenuation is knownin a small number of frequency bands. In this article, theattenuation of sound is measured at 42 frequency bandsuniformly distributed on the near-logarithmic EquivalentRectangular Bandwidth (ERB) rate scale. The same fre-quency representation is also used in current loudness mod-els [2].The signal paths in a system consisting of two devicesare illustrated in Fig. 1. The observed microphone signalis y(n). The original signalx(n) played from the loud-speaker of Device A is also basically available to DeviceB over the network link. Therefore, it is possible to findan estimate for the acoustic transfer functionH(ω) =Y (ω)/X(ω), whereY (ω) andX(ω), are the Fourier trans-

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forms of the signals. The off-line method of performingthis is often called deconvolution, and the online algorithmconverging to the same solution is the adaptive filter. Inboth cases it is necessary to sendx(n) over the networkfor the actual computation, which increases the networkload. In this paper we use an adaptive filter as a referencemethod for path estimation and compare that to a simpli-fied method where the network load is only a fraction ofthat.

3. THE EXPERIMENT

The experimental setup is based on simulations of the prop-agation of sound in a building. For this, an algorithm isused which calculates a transfer path from a source in oneroom to a receiver in an adjoining room. This is performedat the sampling rate of 44.1 kHz. The calculation is basedon known properties of building materials and the con-nections between them. Only transmission paths with nomore than one junction are considered. At each junctionthe sound energy is reduced by app. 10 dB. The reverber-ation in the receiving room is also included in the sim-ulation. As input data, the building products which the2 room situation consist of and the geometrical data aswell as measured room impulse responses are used. It is,thus, rather simple to collect a large amount of examplesfor different ranges of sound insulation and to investigatethe performance of proposed methods for different levelsof reverberation and insulation. The algorithm does notproduce a physically exact sound field but it reproducesthe correct colouration and loudness, which are sufficientfor the current study. A more detailed description can befound in [5].In the current paper we show one typical example repre-senting the attenuation of sound in propagation from a liv-ing room to a bedroom room which has the reverberationtime of T60 = 0.3s. The background noise in the receiv-ing room is modelled by a pink noise sequence added tothe target signal.

4. FREQUENCY-DOMAIN ADAPTIVE FILTER

In model-based system identification the acoustic path isapproximated as a linear system, for example, an adaptiveFIR filter. In this paper we use frequency domain adaptivefilter (FDAF), see, e.g., [6] for a review. FDAF is knownfor a good performance in various applications of acousticsignal processing. The implementation used in the currentarticle is aconstrainedalgorithm based on the overlap-save method and computes the error in the time-domain.The gains of the adaptation step size matrix are adaptedindividually in the control of the variance of the values.This makes the algorithm well suited for a complex case

Figure 2:The subband analysis system based on compar-ison of subband signal energies.

of low signal to noise ratio and distracting sources.For comparison, the obtained frequency domain weightsW(k) can be mapped to the ERB scale to establish theconnection to loudness models.

5. COMPARISON OF SUBBAND ENERGIES

The traditional way of measuring the sound propagationin a dwelling is based on (third) octave band energy anal-ysis. For example, van den Eijk introduced a sophisticatedsystem for the characterizing the disturbance of theneigh-bour’s radio in 1959 [7]. The measurement system hadeight octave band filters each followed by a thyratron cir-cuit and a column of counting devices. Each counter startedcounting when rectified signal level exceeded 65, 70,· · · ,and 90 dB, respectively. After a measuring period the countervalues divided by the total count in each column to pro-duce a cumulative amplitude probability density functionat each octave band. The derivative of that gives the prob-ability density function (PDF) of level estimates. The vanden Eijk’s machine can be turned into a device for pathidentification by replacing the levels by level differencesbetween the original and the observed microphone sig-nal. In this paper this is called subband energy analysis(SBEA) method.The algorithm tested in this article runs at the samplingrate of 44.1 kHz. The microphone signal and the originalsignal are split into 42 frequency bands where the band-width of each band is one ERB. The processing for oneband is shown in Fig. 2. The energy envelope within eachband is computed with the temporal resolution of 10 msand sent over the network to the receiver. There the micro-phone signal is processed similarly. The time differencesbetween the subband envelopes of the original signal andmicrophone signal are then compensated by finding themaximum peak of the normalized cross-correlation func-tion between the envelopes. The envelopes correspondingto the original signal are then delayed so that the level dif-ferences can be computed by simple the subtraction of the

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Figure 3:The PDF of level differences obtained from thesubband analysis system in the case of sound propagationfrom one room to another with a 20 dB SNR. The truetransfer function is plotted in the figure.

decibel-valued short-time envelopes. In the current articlethe frame size for the envelope processing is one second,that is, only 100 envelope values at each subband.Each time a certain level difference value is registered, thecorresponding counter in the statistics unit (see, Fig. 2) isincremented. The increment step size is adaptive and itis a function of the magnitude of the maximum peak inthe normalized cross-correlation function. After an analy-sis period, the unnormalized PDF of level differences canbe read from the counters. The PDFs obtained using a10 s segment of music are shown (dark gray representshigh probability) in Fig. 3. The original response com-puted from the impulse response is plotted in the samefigure. The microphone signal was produced using Setup1 introduced below with a pink noise background distrac-tor at the dBA level of 20 dB below the music.In this article the maximum position of the PDF withineach subband is selected as the estimate for the attenua-tion. The attenuation of sound at different ERB bands inthe simulated setup is shown in Fig. 4 (solid curve). Thepanels represent the cases where the level difference be-tween the sound leaking from the other room is 30, 20, 10,and 0 dBA above the level of the pink background noise.The estimates obtained with SBEA (dashed) and FDAF(dotted) method are plotted in the same figure.At high levels of leaking sound both methods give verysimilar estimates for the attenuation. Closer to the noisefloor, SBEA underestimates the true amount of attenuationwhile FDAF is capable to follow slightly below the noisefloor. However, above the noise floor the two methods arecomparable.

Figure 4:Attenuation of sound between a living room anda bedroom (solid), and the estimates obtained with SBEA(dashed), and FDAF (dotted) algorithms for four differentSNR values. The responses were measured using a 10sfragment of hard rock music.

6. IDENTIFICATION BY EMBEDDEDWATERMARKS

Often there are two or more devices rendering the sameaudio material, for example, all neighbors watching thesame TV program. Therefore, the identity of the devicewhich is heard in the microphone signal remains unclearin both methods described above.We propose using a very similar technique that is com-monly used in audio watermarking. We imperceptibly em-bed a unique watermarkvi (a periodic random white noisesequence, with a period length of, say,L) into each de-vice signalxi using a psycho-acoustic model such thatthe spectrum level of the watermark follows the maskedthreshold in time and frequency. In the receiver the de-vice is then identified by means of a watermark detector;the received signaly is correlated with all cyclicly shiftedwatermarksvi. If the highest correlation, in terms of stan-dard deviation, exceeds a thresholdT then it is assumedthat the microphone signaly captured the signalxi.In more detail, devicei plays the signalxi = xi + vi,where it is assumed that the audio signalsxi are uncor-related with the watermarkvi. Moreover it is assumedthat the watermarksvi and its cyclicly shifted versionsare uncorrelated as well (at least, the correlation is small).This property makes it possible to distinguish the differentdevices. The microphone captures signaly, which corre-sponds, apart from background noise, to the sum of wa-termarked signalsxi convolved with the corresponding

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acoustic pathshi. Subsequently, this signal is accumulatedin a buffer of lengthL to increase the watermark-to-signalratio, and is correlated with one period of watermarkvi

and itsL − 1 cyclicly shifted versions. This operation re-sults in a correlation bufferbi of lengthL. It is not dif-ficult to show, that this correlation bufferbi contains anoisy version of the acoustic impulse responsehi (pro-vided that the watermark-to-signal ratio is high enoughand the lengthL is large enough to contain the greater partof the impulse response energy). This bufferbi is normal-ized by dividing it by its standard deviation. The highestpeak (in absolute sense) in this buffer is compared witha thresholdT (in our experiment we choseT = 5). Ifthis peak exceeds this threshold then it is decided that themicrophone signaly contains the audio signalxi (in fact,we assume here that the elements of the normalized corre-lation buffer behave as normally distributed uncorrelatedrandom variables with standard deviation equal to one).Moreover, since the the correlation buffer contains the im-pulse responsehi (although noisy), it is possible to makean estimate on which signalxi is the most disturbing.Alternatively, the embedded watermarksvi can be used asthe far-end signal of an adaptive filter (we used the FDAF)with the microphone signaly as the input signal. If theenergy of the watermarkvi in the microphone signaly islarge enough then the filter weights of the adaptive filterresembles the acoustic pathhi.The performance of the watermarking method for threedifferent types of music material is illustrated in Fig. 5.The curves represent the value of the maximum of the es-timated response as a function of the distance to the pinknoise floor. If the threshold is set to 5, the source can be de-tected reliably in spectrally rich heavy music (Entombed)even at very low levels, but in highly tonal banjo jazz (BelaFleck) and symphonic music (Bruckner) samples the de-tectability is low when SNR is below 30 dB. The differ-ences result from the different amplitudes of watermarkdata in the three signals.

7. CONCLUSIONS

In this article we compared two methods for online esti-mation of the attenuation of sound in propagating fromone room to another in a building. The first method usesadaptive filtering and the second method is based on sim-ple comparison of amplitudes of ERB subband envelopes.It was found that the two method produce similar resultsfor signals above the background noise floor in a simu-lated system for sound insulation in a dwelling. Secondly,we evaluated the performance of a method for the identi-fication of a sound source from a microphone signal. Themethod which was based on embedding a watermark sig-nal shaped by the masked threshold curve was found to

Figure 5:The value of the maximum peak as a function ofthe distance of the leaking signal to the pink noise floor.

work well for music signals, although, the performancedepends very much on the type of the audio signal.

8. REFERENCES

[1] B. Berglund, T. Lindvall, and T. H. Schwela, “Guide-lines for community noise,” Report, World Health Or-ganization (WHO), Geneve, Switzerland, 1999.

[2] B. C. J. Moore and B. R. Glasberg, “A revision ofZwicker’s loudness model,”Acustica - Acta Acustica,vol. 82, pp. 335–345, 1996.

[3] Tom Blank, Bob Atkinson, Michael Isard, James DJohnston, and Kirk Olynyk, “An internet protocol (ip)sound system,” inProc. 117th AES Convention Paper,San Francisco, CA, USA, October 2004.

[4] M. Jeronimo and J. Weast,UPnP design by exam-ple - software developers guide to Universal Plug andPlay, Intel Press, 2003.

[5] M. Vorl ander and R. Thaden, “Auralisation of air-borne sound insulation in buildings,”ACUSTICAunited with ACTA ACUSTICA, vol. 86, no. 2, pp. 76–89, 2000.

[6] J. J. Shynk, “Frequency-domain and multirate adap-tive filtering,” IEEE Signal Processing Magazine, pp.14–37, January 1992.

[7] J. van den Eijk, “My neighbour’s radio,” inProc. 3rdInt. Congress Acoustics, Stuttgart, Germany, 1959,pp. 1041–1044.

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