Acoustic path database for ANC in-ear headphone development
Stefan LIEBICH, Johannes FABRY, Peter JAX, Peter VARY
1 Institute of Communication Systems, RWTH Aachen University, Germany, {liebich, fabry, jax, vary}@iks.rwth-aachen.de

Abstract
Active noise cancellation (ANC) for headphones is an emerging field with growing interest in consumer applications. Developing such an ANC system involves modeling the system from measurements, designing thealgorithms and simulating the performance before building a real-time system. Modeling the system considers
the transfer functions between loudspeaker and microphones, called secondary and feedback paths, as well as
the relative transfer function between the microphones, called primary path. These paths are modeled with varying degrees of accuracy in the development process: From coarse physical models, e.g., with pure delay; overmeasured paths under ideal conditions, called nominal; to considering uncertainty due to various operating conditions. However, very few measured paths of ANC headphones publically available. Hence, this publication
describes an alongside published database containing two measurement sets of the Bose QC20 in-ear headphone
without ANC electronics. The first set contains primary, secondary and feedback paths of 23 subjects measured
in an acoustic booth. The second set contains primary paths measured for equally distributed directions on ahorizontal plane acquired in a semi-anechoic chamber with a dummy head. The database can be used to consider
inter-person differences and direction-of-arrival dependency in algorithm design and simulation.
Keywords: Active Noise Control, Active Noise Cancellation, ANC, Acoustic Paths, Database

1

INTRODUCTION

Real-world audio signal processing problems are usually very complex. They are depending on the acoustic
front-end, e.g. a specific headphone design, and often involve time-varying acoustic conditions. Thus, various
simplifying steps are made during the development of audio signal processing algorithms. For the developmentof an ANC system a four step approach, which might be applied recursively, is suggested by Elliott [3]:1. Simulation by using simplified analytical models
2. Simulation with measured acoustic paths under ideal control conditions, i.e. only nominal paths.
3. Simulation with measured acoustic paths under a variety of operating conditions, i.e. perturbed paths
including deviations from nominal path.
4. Implementation of a real-time system and testing under all conditions, to verify predicted behaviour.
The different steps from 1. to 4. involve increasing complexity and they step by step lead to a real-time system.
In ANC publications, often very simple and unrealistic system models are used, such as unit delays. They might
be adequate for explaining certain behaviours, but are certainly not useful for demonstrating real performance. At
the same time very few measurements of actual ANC transmission paths are publically available. This gap isaddressed by our publication, which describes a database containing a large number of measurements with a
Bose QC20 ANC headphone without ANC electronic, both with human subjects and a dummy head.
The database is named IKS Paths for Active Noise Cancellation Development And Research (iks | PANDAR) andavailable under http://iks.rwth-aachen.de/PANDAR/. Before describing the measurement setups, the results
and the database structure, a short background in ANC is given in the following section.

2

ANC BACKGROUND

To actively attenuate an acoustic disturbance signal, an ANC system creates a control signal which is emitted bya loudspeaker. This control signal destructively interferes with the disturbance signal. To acquire information

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about the disturbance, an ANC headphone typically contains an outer microphone, recording the outer disturbance,
and an inner microphone capturing the signals within the ear canal. When using only the outer microphonesignal, the system is called a feedforward systems [3]. When only the inner microphone is used, the system
is called a feedback system. Fig. 1a gives an overview of an the underlying signal processing aspects for an
in-ear headphone. The illustration shows a combined feedforward-feedback approach. On the acoustic side,

x(n)

d(n)

P(z)

e(n)

+
G(z)

+

F(z)
W (z)

y(n)
yw (n)+yk (n)K(z)a0 (n)(a) Structure.

(b) Discrete model.

Figure 1. ANC headphones structure and discrete model.
three transmission paths are of interest: the primary path P(z) (outer to inner microphone), the secondary path
G(z) (speaker to inner microphone) and the acoustic feedback path F(z) (speaker to outer microphone). Fig. 1aonly illustrates the acoustic components of these transmission paths for the sake of readability. The detailed
components are outlined in the next section and shown in Fig. 2. On the electronic side, the control signal y(n)is a combination of the feedforward control signal yw (n) and the feedback control signal yk (n). yw (n) is createdby filtering the outer disturbance signal x(n), acquired by the outer microphone, with the feedforward filter W (z).
yk (n) is acquired by filtering the error signal e(n), acquired by the inner microphone, with the feedback filter(also known as feedback controller) K(z). A desired audio signal a(n), e.g. a music or a telephone signal, canalso be inserted into the control signal. It might require additional prefiltering Wa (z) to account for loudspeakercharacteristic and influence of the feedback control. From this, the discrete model in Fig. 1b can be deduced forthe filter design. Without loss of generalization we assume a(n) = 0. Based on the model, the overall transfer
function from x(n) to e(n) is
1

E(z) P(z) − G(z)W (z) 1+F(z)W (z) F(z)≈0 P(z) − G(z)W (z)
=
≈ =
,
1
X(z)
1 + G(z)K(z)1 + G(z)K(z) 1+F(z)W
(z)

(1)

with assuming the acoustic feedback path can be neglected (F(z) ≈ 0) for the headphone applications. Then, the!

P(z)ideal feedforward controller for E(z) = 0 is given by W0 (z) = G(z). More details on the digital implementationof W0 (z) is, e.g., given in [4] or [6]. This time-invariant approach offers compromised performance when thereal P(z) and G(z) deviate from the nominal paths used in the design. The deviation is known as uncertainty.
Therefore, time-variant methods which adapt to the current situation are getting increased interest in recent years.
Feedback control requires more sophisticated methods, which explicitly consider the stability of the feedback loop
in the presence of uncertainty about the acoustics of G(z). One example is the H∞ -optimization as published in[7].
In general, all design methods require good knowledge about P(z), G(z) and F(z). This includes the nominal

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case, but also the uncertainty due to varying acoustic conditions. Especially for evaluating new algorithms it is
crucial to have an accurate simulation of a real ANC system.

3

MEASUREMENT METHODS

For measurements of the secondary path G(z) and the acoustic feedback F(z), the inner loudspeaker is emitting
an excitation signal y(n). For the primary path P(z) an external sound source yext (n) is necessary, as P(z) is therelative path between the inner and the outer microphone. Practically, two individual paths from the external
speaker to the inner and outer microphone are measured: Qin (z) and Qout (z). They are later related to each other.
An overview of all paths is given in Fig. 2. The acoustic component of each path is denoted by ∗A .

Figure 2. Transfer function components and measurement structure.
For the filter design, the primary path P(z) is the transfer function between the digital outer disturbance anderror signals, x(n) and e(n). It represents the passive attenuation of the headphone, however, is influenced by the
microphone characteristics Hmic,in (z) and Hmic,out (z), yielding
HDAC (z)Hspk,ext (z)Qin,A (z)Hmic,in (z)HADC (z)Qin (z)=
Qout (z)HDAC (z)Hspk,ext (z)Qout,A (z)Hmic,out (z)HADC (z)Hmic,in (z)=· PA (z).
Hmic,out (z)P(z) ==

Hmic,in (z) Qin,A (z)·Hmic,out (z) Qout,A (z)

(2)
(3)

Thus, the primary path for the filter design P(z) includes the acoustic component of the primary path PA (z) andthe ratio between the two microphone characteristics, Hmic,in (z) and Hmic,out (z). The analog-to-digital (AD) anddigital-to-analog (DA) converters are each assumed to have identical characteristics for all channels, HADC (z) andHDAC (z).
The secondary path G(z) is the transfer function between the control signal y(n) and the error signal e(n). It
describes the influence of the control signal on the residual inside the ear canal, as
G(z) = HDAC (z) · Hspk (z) · GA (z) · Hmic,in (z) · HADC (z)= HEB (z) · GAF (z),

(4)
(5)

with the electronic backend GEB (z) = HDAC (z) · HADC (z) and the acoustic front-end GAF (z) = Hspk (z) · GA (z) ·Hmic,in (z). HADC (z) and HDAC (z) contain the influence of the AD/DA converters as well as the necessaryanti-aliasing filters.

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The acoustic feedback path F(z) is similarily defined as
F(z) = HDAC (z) · Hspk (z) · FA (z) · Hmic,out (z) · HADC (z)= HEB (z) · FAF (z).

(6)
(7)

It describes the leakage of the control signal to the outer microphone. The acoustic feedback needs to beconsidered for the design of a feedforward filter, when not sufficiently small due to bad design of the acoustic
front-end.
3.1 Measurement Setups
The acoustic paths within this database were acquired in two different setups visualized in Fig. 3.

(a) In Acoustic Booth.

(b) In Semi-Anechoic Chamber.

Figure 3. The two measurement setups.

Acoustic Booth The first setup, is an acoustic booth used for listening tests (STUDIOBOX Premium). P(z),G(z) and F(z) were measured during a listening test to evaluate the subjective performance of an active occlusioncancellation system [5]. The booth has an average attenuation from outside to inside of 44 dB. Its main purpose
is to provide an acoustically isolated listening environment with a low reverberation time. It is not designed
for free-field measurements and thus suffers from reflections visible in the primary paths later. However, thebenefit of this dataset is the availibility of associated sets of P(z), G(z) and F(z) for 23 persons (left and rightside). Additionally, two handling cases were measured, including the situation open ("lying on the table") andclosed ("closing the sound outlet"). Both were measured twice for both ears, resulting in four measurementsof each case. The measurements were performed with a dSPACE DS1005 real-time system with DS2004 andDS2102 AD/DA-extension boards. A reference measurement of this backend is part of the database, as describedin Sec. 4.1. The sampling rate for the these measurements is fs = 48 kHz and the impulse responses has 194400
samples measured with a 4.05 s long logarithmic sweep signal.
Semi-Anechoic Chamber Due to the large impact of reflections on the primary path measurements, a second
dataset is included in this database. The second measurement set was originally acquired in a semi-anechoic
chamber for an investigation of direction-of-arrival dependency of ANC systems [6]. The chamber has a concrete
floor with a well-defined reflection. Apart from this floor reflection, it offers acoustic free field conditions above
100 Hz for acoustic measurements. The headphones were inserted into a Head Acoustics dummy head withintegrated acoustic ear simulator (HMS II.3 with 6460 MFE VI amplifier, HEAD Acoustics GmbH, Herzogenrath,
Germany). The headphone microphone signals were recorded with an RME soundcard to deduce P(z). The
database contains measurements for 72 different directions starting from a frontal position and with 5◦ resolution
on horizontal plane. The sampling rate is fs = 48 kHz and the impulse responses have 131072 samples measured
with a 2.731 s long logarithmic sweep signal, each. In a separate step, but with the same fitting, G(z) and F(z)4329

were acquired with the dSPACE DS1005 real-time system. Here the impulse responses have 194400 samples
measured with a 4.05 s long logarithmic sweep signal.
The external speaker for both cases was a Neumann KH120A (linear magnitude spectrum from 52 Hz to 21 kHz± 3 dB). In both cases, the measurement system was placed outside the acoustic booth to minimize ambient
noise.
3.2 Processing
The excitation signal was a logarithmic sweep. For the measurements in the semi-anechoic chamber as wellas the post-processing the open source ITA Toolbox [2, 1] was used, which is developed at the Institute ofTechnical Acoustics, RWTH Aachen University. The database contains the unprocessed transfer functions, which
only have been divided by the spectra of the excitation signals. For the visualization, the transfer function were
postprocessed with a Hann window in the time domain. The cutoff times are indicated with the individualtransfer functions in the later sections.
3.3 Tagging
The measurements are separated into use cases and handling cases, as the headphone might only require toprovide performance while worn by the user. The use case measurements are additionally labeled based on thefitting of the headphone. The labels were chosen as normal-fit, slightly-loose-fit and loose-fit by observing themagnitude of the secondary paths at 90 Hz. The bounds were heuristically chosen to [∞, 0.8 dB], [0.8 dB, −5 dB]
and [−5 dB, −∞], based on the air leakage of the ear canal cavity. Handling cases include the situation open("lying on the table") and closed ("closing the sound outlet").

4

MEASUREMENTS

In the following section, the measurements included in the database are visualized and described. All magnitude
and phase responses were individually smoothed by 1/24th octave band smoothing filter for visualization.

Phase [deg]Magn. [dB]

4.1 Electronic Back-End
Fig. 4 shows the magnitude and phase the backend for three different configurations. The dSPACE DS1005
real-time system with DS2004 and DS2102 extension boards by itself has a latency of 1 sample at 48 kHz(roughly 20.8 µs), shown as (). The microphones require a DC bias voltage, which needs to be removed
by a low-cut filter, visible by (). In our system it is realized as a digital first-order IIR filter. Also, thesystem uses succesive approximation register (SAR) converters, which require a dedicated analog anti-aliasing
filter (AAF), shown by (). The preamplification and the DC bias voltage for the microphones were provided
by a RME Micstasy preamplifier (preamp), shown by (). The system was used with the last configuration
20
10
0

20
180
90
0
−90
−180
20

Direct40 60 100

200

40 60 100200

LowCut

400

AAF

1k

Preamp2k

4k 6k 10k

20k

400
1k
2k
Frequency [Hz]4k 6k 10k

20k

Figure 4. Measurements of the electronic backend.

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Magnitude [dB]
Phase [deg]

20
−40
0
an
e
−60
m
−20
−80
n
−40
persons
open
persons
open
mea
−100
−60
closeddummyclosed
dummy
−80
−120
20 40 60 100 200 400 1k 2k 4k 6k 10k 20k 20 40 60 100 200 400 1k 2k 4k 6k 10k 20k
180
180
90
90
0
0
me
me
an
an
−90
−90
−180
−180
20 40 60 100 200 400 1k 2k 4k 6k 10k 20k 20 40 60 100 200 400 1k 2k 4k 6k 10k 20kFrequency [Hz]Frequency [Hz](a) Secondary Path G(z).

(b) Acoustic Feedback Path F(z).

Figure 5. Measurements of the secondary and acoustic feedback paths.
with low-cut, AAF and preamplifier. The complete system has a latency of roughly 37 µs, increasing towards
low frequencies due to the low-cut. The reference measurements are provided to allow for correction of theelectronic backend in the secondary and the acoustic feedback path. This can be realized by simple spectral
division. The following transfer functions still contain the electronic backend and thus show G(z) instead ofGA (z) and F(z) instead of FA (z).
4.2 Secondary Paths
The different secondary paths are shown in Fig. 5a. The impulse responses were processed with a one-sided
Hann window starting after 150 ms and ending at 160 ms. From the acoustic booth measurements the different
persons with various fittings () as well as the handling cases closed () and open () are shown. The
complex mean () considers all these measurements. Additional to that, the dummy head measurements ()
from the setup in the semi-anechoic chamber are shown for comparison.
One can clearly see that the sound pressure provided by the speaker at low frequencies depends on the fitting.
The larger the leakage of the headphone is, the less energy can be emitted at frequencies below 1 kHz. Directly
closing the sound outlet of the headphone on the other hand allows for high energies at low frequencies. It also
creates a resonance shift from 4 kHz to 3 kHz. This uncertainty in G(z) poses a challenge for feedback andfeedforward filter design.
4.3 Acoustic Feedback Paths
The different acoustic feedback paths are shown in Fig. 5b. The measurements were very noisy, as the acoustic
feedback is relatively low in the Bose QC20. Higher sound pressure levels (SPL) of the excitation signal should
be avoided to avoid harming the human subjects and avoiding to damage the speaker. Therefore, the impulse
responses were processed with a very short one-sided Hann window starting after 4.5 ms and ending at 5 ms.
Due to the short window size only frequencies above f = 1/(5 ms) = 200 Hz contain relevant information.
4.4 Primary Paths
The primary path measurements are visualized in Fig. 6a for the acoustic booth and in Fig. 6b for the semianechoic chamber. The impulse responses were processed with a one-sided Hann window starting after 150 ms
and ending at 160 ms. In general, both measurements show the expected low pass characteristic of P(z) withvarying cut-off frequency between 200 and 600 Hz for all but the open case. The open () and closed ()
case in Fig. 6a reveal that these cases need to be considered carefully, as they include a large amount of noise.

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Magnitude [dB]
Phase [deg]20
20
mean (Cmpl.)
00mean (Magn.Phase.)
mean
−20
−20
persons
−40
open
−40
−60
directions
closed−60−80
20 40 60 100 200 400 1k 2k 4k 6k 10k 20k 20 40 60 100 200 400 1k 2k 4k 6k 10k 20k
180
180
90
90
mean (Magn.Phase.)00
mean
mean (Cmpl.)
−90
−90
−180
−180
20 40 60 100 200 400 1k 2k 4k 6k 10k 20k 20 40 60 100 200 400 1k 2k 4k 6k 10k 20kFrequency [Hz]Frequency in Hz(a) Primary Path (Booth).

(b) Primary Path (Anechoic). 72 Directions.

Figure 6. Measurements of the primary paths.
Before being used, they might require additional smoothing.
Fig. 6b, also reveals one issue with the complex mean () value at high frequenices. When the phase is
becoming very uncertain at high frequencies, the complex mean results in non-intuitive results [8]. One possible
solution is to calculate a separate mean on magnitude and phase (), which requires phase unwrapping. Thisis especially difficult in the presence of notches in the magnitude spectrum, which lead to discontinuities in thephase (e.g. Fig. 6a around 600 Hz for open ()). A robust solution still needs to be proposed for this problem.

5

DATA FORMAT AND ACCESS
ans

ans
PANCACE database Bose QC20

.ITA_TOOLBOX_AUDIO_OBJECT... same as struct ...

.comment.freqData (nBins x nChannels)

handling (4 files).domain (’freq’ or ’time’).freqVector (nBins x 1)

persons (23 files).data (datapoints x nChannels).timeData (nSamples x nChannels).samplingRate.timeVector (nSamples x 1)

primary (72 files).channelNames.nBinssecondary+afb (1 file).userData.nSamples

...

...

acoustic_booth

anechoic_chamberelectronic_backend (1 file)(a) Folder structure.

(b) Data struct(ans=load(file,'-mat')).

(c) ITA object(ans=ita_read_ita(file))

Figure 7. Data structure.
Fig. 7 visualizes the structure of the database. On the left (Fig. 7a), the folder tree is shown, indicating where tofind the different measurement files. The middle (Fig. 7b) gives a hint on the MATLAB struct, when loading thefile via load(file,'-mat'). The file contains a MATLAB struct named .ITA_TOOLBOX_AUDIO_OBJECT.
The data is accessible via .data and is represented either as real time domain data or as complex one-sided

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frequency domain data, indicated by the .domain entry. It contains various additional metadata, including.comment, .channelNames, .userData, .samplingRate, etc. Without using the ITA toolbox, it is possible
to extract the data, based on the .data and .domain entries. When using the native ITA toolbox command
ita_read_ita(file), this struct is directly converted into an itaAudio object. Some of the additional fields
are shown in Fig. 7c. The ITA toolbox offers a more convient interface, e.g. by automatically transforming
between time and frequency domain depending on the requested output. Furthermore, the ITA toolbox offers
various powerful visualization and post-processing tools, including spectral inversion with regularization, time
domain windowing, smoothing functions, system identification, etc.

6

CONCLUSIONSThis paper describes the iks | PANDAR database, which contains measured paths for filter design, systemsimulation and evaluation of ANC algorithms. It offers 46 primary, secondary and acoustic feedback pathmeasurements from human subjects, 8 path measurements of handling cases, as well as primary path measurements for a dummy head for 72 different directions, with the associated secondary and acoustic feedback path.
Reference measurements of the electronic backend are also included. The database is available for download
under http://iks.rwth-aachen.de/PANDAR/.

ACKNOWLEDGEMENTS
The authors thank the Institute of Technical Acoustics (ITA), especially Jan-Gerrit Richter and Prof. Michael
Vorländer, for their support during the measurements in the semi-anechoic chamber.

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