## 3 Data Acquisition
### Measurement and Preprocessing  
The ECIR was measured according to the process shown in Figure 2. To make it easier to reproduce the experiment, a picture of the actual measurement is shown in Figure 3. The earphone used was a SoundTrue Ultra (Bose Corporation, Framingham, MA, USA). Glue was used to attach a small microphone MAA-03A-L60(STAR MICRONICS Co., Ltd., Shizuoka, Japan) to the earphone. The earphone was chosen because it has ear tips that are shaped to fit the ear concha. We hypothesized the ear tips would reduce fluctuations in earphone position caused by putting on and taking off. We selected small microphones with characteristics in the audible range of 20 Hz to 20 kHz. The MLS signal was output from Quad-CAPTURE (Roland Corporation, Shizuoka, Japan). Sound level meter TYPE6226 (ACO Co., Ltd., Miyazaki, Japan) was used as a microphone amplifier, and the amplified microphone signal (reflected sound) was recorded on a PC using Quad-CAPTURE. The MLS signal (signal length of \(2^{14}-1\)) was measured and played five times to obtain an average noise level of approximately 65 dB (A). The sound levels were measured using a modelled head before measuring the ECIR. The sensitivity of the sound processor was set as high as possible without saturating the waveform. The MLS signal was output five times successively, and the reflected sound was processed as an additive average. The impulse response (ECIR) was derived from the additive-averaged reflected sound using the Hadamard transform. Additive averaging can be used to measure ear acoustic characteristics more accurately [12]. For the derived impulse response, a minimum phase-system transformation was performed using the Hilbert transform to ensure that the impulse response component is at the beginning of the signal. Because the signal component is at the beginning of the signal, a minimum phase-system transformation was performed to make the clipping process easier. After minimum phase-system transformation, normalization was performed. The measurements were made over several days, during which the volume and sensitivity knobs of Quad-CAPTURE may have changed slightly. The magnitude of the signal may vary due to slight differences in the volume and sensitivity between the day of measurement. To allow for this variation in the data, normalization is a popular method used in machine learning. Normalization is defined as follows:  
\[signal^{\prime}(n)=\frac{signal(n)}{\sqrt{\sum_{n=1}^{N}signal(n)^{2}}}\qquad (n=1,2,\cdots,N) \tag{1}\]  
where \(n\) indicates the point of the samples and \(N\) is the total length of the signal. \(signal(n)\) indicates the signal after a minimum phase-system transformation. After normalizing, a clipping process was performed. Because the impulse response component was 200-300 samples from the beginning of the signal, 256 samples were clipped out to extract the signal components. A total of 256 samples were determined based on the authors' experience with the waveform. Finally, bandpass filtering (100 Hz-22 kHz) was performed to remove the direct current component.  
The measurements comprise two sessions, with each session consisting of 30 trials. In the first session, measurements were performed for 30 trials without removing the earphones. In the second session, earphones were attached and detached for each trial, and measurements were taken.